Optimal encoding of non-stationary sources Abstract

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Proceedings of the InternationalConference on Mechanical Engineering 2003(ICME2003) 26- 28 December 2003, Dhaka, BangladeshICME03-AM-46APPLICATION OF ERGONOMICS IN SHIP DESIGNOmar bin Yaakob and Lim Shiau NeeFaculty of Mechanical EngineeringUniversiti Teknologi Malaysia,81310 UTM Skudai,MalaysiaABSTRACTReports on shipping casualties show the persistence of a poor maritime safety record and despite theinfluence of the technical degradation of an ageing fleet, the fact remains that the human factors areresponsible for the majority of shipping accidents. Ship designers can play a role in reducing factors thatmay lead to fatigue and hence human errors in operation of ships and its equipment. Proper ergonomicsdesign of ships is important from safety and comfort aspects. This paper reviews the application ofergonomics; particularly habitability standards in ship design and presents a case study of its implementation on a Malaysian patrol vessel. A patrol vessel was chosen and measurements were madeand compared with standard ABS guidelines. The results shows that in most areas, the design of the patrolboat fails to comply with ergonomics design guidelines.Keywords: human factors, patrol boats, habitability1. INTRODUCTIONErgonomics is the study of the interaction of humans and their environment. Many engineering systems, which may not have appropriately considered the human element, have been shown to contain features that can lead or have led to errors committed by humans duringconstruction, maintenance and/or operation. Therefore, it is important to consider the ergonomics aspects when designing the components and systems such as that it is safe for human to construct, maintain and operate. Proper ergonomics design of ships is no exception. Reports on shipping casualties show the persistence of a poor maritime safety record and despite the influence of the technical degradation of an ageing fleet, the fact remains that the human factors are responsible for the majority of shipping accidents. This paper review the application of ergonomics in ship designs and presents a case study of its implementation on a Malaysian patrol vessel.2. HUMAN FACTORS IN SHIP DESIGN2.1 INTRODUCTIONIt is often stated that human element accounts for at least 80% of all catastrophic marine casualties [1]. The importance of addressing the human element in maritime safety has been recognised by the International Mari time Organisation (IMO). However, IMO’s primary effort so far have concentrated on operations, management and training issues. This has led to the implementation of International Safety Management Code (ISM Code) and the 1995 amendments to the Standard for Training, Certification and Watch keeping Convention (STCW 95) [2].IMO has also devoted some attention to the human element during design. The sub-committee on Design and Equipment is currently developing the Guidelines for Engine-Room Layout, Design and Arrangement. The subcommittee on Safety of Navigation has a correspondence group working on Ergonomic Criteria for Bridge Equipment and Layout. However, these efforts are not as comprehensive as those previously devoted to safety management and training [2]. The efforts towards improving working conditions in land-based working areas have been more forth coming. In Malaysia for example, the Occupational Safety and Health Act [3} was promulgated in 1994. Amongst others, the Act stipulates a number of measures that employers must take to ensure safety, health, comfort and welfare of their employees. However the law does not apply to ships in operation.2.2 Human FatigueHuman fatigue has been identified as the primary cause and a major contributing factor of numerous maritime mishaps, such as Exxon Valdez and Herald Of Free Enterprise [2]. Unfortunately, most of ship design and construction rules, such as those published by classification societies, do not adequately address this human element. The guidelines allow for harsh shipboard environments that are noisy, dimly lit, and have high levels of vibration. These conditions disrupt sleep, cause fatigue and intensify its effects.Adequate sleep is important for operational effectiveness of the crew. Unfortunately, most shipboard operators are not able to get this much sleep. According to Ref. [4], almost 50% of Australian seafarers, while underway, only had four to six hours of sleep a night. Consecutive nights of short sleep duration results in the development of a cumulative sleep debt. This condition lowers initial energy levels and increases the effects of fatigue felt throughout the day, sometimes leading to human errors with disastrous consequences. A proper sleeping environment is critical in ensuring that sleep is restorative. The design of the shipboard sleeping environment is directly controlled by naval architects and marine engineers and as such, they havea great role to play.3. FACTORS FOR ERGONOMICS SHIP DESIGNBy far, the most comprehensive guidelines for ergonomics design is that recommended by American Bureau of Shipping (ABS) [2]. The guidelines cover such aspects as proper design and layout of the workspace and creation of a conducive working and living environment for the crew. Details regarding habitability standards are given in [5]. Proper design of workstation, recreational, work and sleeping environment will contribute towards alertness on watch and reduction of fatigue. For that purpose, four design factors must be considered viz. lighting, noise, vibration and indoor climate [2]. Ref [4] added ship motion as a factor whilst this is already considered as the low-frequency part of whole body vibration described in ABS guidelines [2,5] 3.1 NoiseNoise is present in most compartments of a ship and it is difficult to avoid. Noise comes from numerous sources including engines, generators, pumps, and air conditioners. Mariners working in a noisy environment tend to be moody, irritable, and unable to effectively deal with minor frustrations. Noise causes blood pressure to go up, increases heart and breathing rates, accelerates the metabolism, and a low-level muscular tension takes over the body ("fight or flight" effects). If the noise continues for long periods, the factors compound and it becomes harder to relax. The factors increase as the noise levels increase [4].The effect that noise has on sleep challenges designers of shipboard general arrangements. Finding the optimal location for sleeping quarters and crew recreation compartments is critical. The levels recommended by American Bureau of Shipping (ABS) [2] are shown in Table 1.3.2 LIGHTINGShipboard operators work in a 24-hour environment. Watch schedules frequently change and individuals work under incandescent or florescent lighting throughout the night. Unfortunately, the lighting that is typically installed aboard ships is not stimulating and Current design standards and guidelines on illumination levels in ship compartments are inadequate for maintaining watch-stander alertness and do not mitigate fatigue. ABS guidelines for lighting provide recommended illumination levels for all the types of compartments on a ship.3.3 VibrationMariners experience shipboard vibrations caused by machinery, marine equipment and the ship’s response to the environment. Vibrations resonate throughout the hull structure and the entire crew can be affected. The propagation of these vibrations along the decks and bulkheads subject the crew to whole body vibration and noise [6].Short-term exposure can lead to headaches, stress, and fatigue. Long-term exposure leads to hearing loss and causes constant body agitation. Maritime vibration guidelines keep levels low enough to prevent bodily injury but the recommended levels can cause fatigue and disrupt sleeping patterns. ABS Guidelines [5] give maximum weighted root-mean-square acceleration level in the frequency range 0.5-80Hz as 0.4 m/s and 0.315 m/s for task-performance and comfort criteria respectively.. 4. CASE STUDY BACKGROUND AND METHODOLOGYFor the purpose of this ergonomics case study, a fast patrol craft belonging to one Malaysian Government agency was chosen to be the subject, Figure 1. The craft is 22.5 metre Aluminium Fast Patrol Craft built in year 2001.In conducting the case study, measurements were conducted onboard the ship to measure allthe environmental condition i.e. lighting level, noise level, vibration level, and thermal condition when the ship was cruising at its normal operating speed. The equipment used for the measurement is shown in Table 2.Measurements were made on 18th January 2002 when the patrol boat was on a regular patrol duty in Malaysian waters. The duration of the study was five hours.5. RESULTS AND DISCUSSION5.1 LightingResults for lighting survey is shown in Table 3. All the shipboard operators of the patrol boat are working in the 24-hour environment and thus the luminance level should be enough to stimulate the body and help to maintain crew alertness. Looking into the comparison between average lux and minimum lux standard from Table 3, it is found that almost all of the average lux values do not even reach to the minimum lux requirement of ABS standard. This situation is considered poor since the duty on watch at night especially need high alertness of officers but the average lux at the wheelhouse is so low that may not help to maintain crew alertness. As for the other areas, the lux levels are markedly quite low as well. Although this is not critical from the safety aspect, the need for comfortable working environment is not fulfilled.As a result, the lighting design of this craft can be said not ergonomic and there are rooms for improvement.5.2 NoiseResults shown in Table 4 indicate that every reading of noise level (dB) has exceeded the maximum level of ABS Standard. Some significant high noise levels from Table 5 are, about 80 dB at wheelhouse, 77 dB at radio room, 102 dB at stern deck, and 120 dB at engine room. These are the main areas where the crew spends most of their time, except engine room that is not continuously manned. Exposure to such high noise level over a period can produce pathological side effects and thus can comprise a health hazard. The excessive noisy environment has provoked crew awareness. Surveys by Lim in Ref [6] indicated that the crew were unhappy5.3 Thermal comfortThe thermal environment is determined by four physical factors: air temperature, humidity, air movement, and temperature of surfaces that exchanges energy by radiation [7]. The combination of these factors determines the physical conditions of the climate and our perception of the climate. According to ABS ergonomics guidance notes [2], the optimum range of dry-bulb temperature for accomplishing light work while dressed appropriately is 21-27 degrees C (70-80 degrees F) for warm climate. The optimum comfortable temperature is 22 degrees C. Meanwhile the humidity should be maintained between 20% ~ 60% with an optimum relative humidity of 45% at 21 degrees C (70 degrees F) if possible. Results of temperature and humidity measurement are shown in Table 5.The result shows, the range of dry bulb temperature is 25 –28 degrees C and the humidity range is 65% – 88%. Compared to the ABS ergonomics standard, in some cases, the temperature and humidity are higher and do not meet the ergonomic range. It is stated that the humidity should decrease with rising temperatures, but should remain above 20% to prevent irritation and drying of the body. Therefore, in order to have a better thermal comfort, the temperature should be decreased to say 21 –26 degrees C.The effective temperature at the engine room was not included in the above discussion since the chart is based on wearing customary indoor clothingand performing light muscular or sedentary work. Although its temperature and humidity is high, it is still in the acceptable zone and it is not critical since the engine room not continuously manned. In the survey by Lim [6], the crew complains of non-uniform distribution of air-conditioning flow. This parameter is indicated in ABS guidelines as vertical and horizontal temperature gradients. However, system in the cabin was not uniformly distributed and that is why they felt uncomfortable. Thus, the flow of the air-conditioning system should be designed to distribute the flow more uniform.The issue that needs attention is the high temperature and humidity in the crew cabin sincehaving a good sleeping environment would help improve the working spirit and reduce fatigue.5.4 VibrationFrom Table 6, RMS values of AZ acceleration are between 0.383 m/sand 1.138 m/s .Comparison with the vibration exposure limits in shows that the ship vibration level is not good, particularly at the stern deck and the engine.Looking into the level to maintain proficiency as an average, it is apparent that the vibration levels at the main working place, wheelhouse and radio room are too high for the crew to maintain proficiency for more than 4 hours. Staying in the engine longer than half an hour is expected to be very uncomfortable.6. CONCLUDING REMARKSMeasurements carried out on a Malaysian Patrol Craft have indicated many areas that need improvement. The designed lighting system on the patrol boat is not ergonomics. It was observed that one reason is the poor distribution of light that is influenced by the reflectance of the walls, ceilings, and other room surface. The grey coloured wall (except the engine room) reflectance is quite low. Thus in order to contribute to the effective distribution and utilization of light, it is desirable to use rather lighter coloured walls, ceilings, and other surface. The noise level measured was high, and thus hearing protectors need to be used with noise level greater than 85 dB. Although noise is an unavoidable issue in maritime operations, steps can be taken in the design stages of a ship to decrease noise effects. Post-production measures can also be taken to reduce noise levels.From the data collected, the humidity in cabin is quite high and may be causing discomfort to the crew. The condition should be improved by reducing the temperature level and directly reduce the relative humidity. The vibration level measured onboard the patrol boat was rather high compared to the ABS Ergonomic Guidance Notes. The source of vibration however has to be identified before applying the solution. Vibrations created by engines, generators, and pumps can be reduced through damping and isolation. The methods used to reduce vibrations are similar to those used to reduce noise.Work reported in this paper is part of an on-going study on implementation of ergonomics guidelines in the design of Malaysian ships. Further work is being done to study other aspects of ergonomics such as anthropometrics as well as carrying out measurements on other types of ships and boats.7. REFERENCES[1]Thomas B. Sheridan and William R. Ferrell, Man-Machine Systems: Information, Control, and Decision Models of Human Performance, The MIT Press, United States of America, 1974.[2]Guidance Notes on the Application of Ergonomics to Marine Systems, American Bureau of Shipping, Houston, April 2003.[3]Government of Malaysia, Occupational Safety and Health Act, Act no: 514, 1994.[4]Calhoun, S. R., Human Factor in Ship Design: Preventing and Reducing Shipboard Operator Fatigue, Society of Naval Architecture and Marine Engineering Chesapeake Section Meeting, 15th December 2003.[5]Guide for Crew Habitability on Ships, American Bureau of Shipping, Houston, December 2001.[6]Lim, S.N., Ergonomics in Malaysian Boat Design, unpublished Final Year Project Dissertation, Universiti Teknologi Malaysia, 2002.[7]Anderson, D. E., Overman, F. R., Malone, T. B., Baker, C. C., Influence of Human Engineering on Manning Levels and Human Performance on Ships, NA VSEA Association of Scientist and Engineers, April, 1996..。

ITERATIVELY WEIGHTED MMSE APPROACH TO DISTRIBUTED SUM-UTILITY MAXIMIZATION FOR INTERFERING CHANNEL

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Consider the MIMO interfering broadcast channel whereby multiple base stations in a cellular network simultaneously transmit signals to a group of users in their own cells while causing interference to the users in other cells. The basic problem is to design linear beamformers that can maximize the system throughput. In this paper we propose a linear transceiver design algorithm for weighted sum-rate maximization that is based on iterative minimization of weighted mean squared error (MSE). The proposed algorithm only needs local channel knowledge and converges to a stationary point of the weighted sum-rate maximization problem. Furthermore, we extend the algorithm to a general class of utility functions and establish its convergence. The resulting algorithm can be implemented in a distributed asynchronous manner. The effectiveness of the proposed algorithm is validated by numerical experiments. Index Terms— MIMO Interfering Broadcast Channel, Power Allocation, Beamforming, Coordinate Descent Algorithm 1. INTRODUCTION Consider a MIMO Interfering Broadcast Channel (IBC) in which a number of transmitters, each equipped with multiple antennas, wish to simultaneously send independent data streams to their intended receivers. As a generic model for multi-user downlink communication, MIMO-IBC can be used in the study of many practical systems such as Digital Subscriber Lines (DSL), Cognitive Radio systems, ad-hoc wireless networks, wireless cellular communication, to name just a few. Unfortunately, despite the importance and years of intensive research, the search for optimal transmit/receive strategies that can maximize the weighted sum-rate of all users in a MIMO-IBC remains rather elusive. This lack of understanding of the capacity region has motivated a pragmatic approach whereby we simply treat interference as noise and maximize the weighted sum-rate by searching within the class of linear transmit/receive strategies. Weighted sum-rate maximization for an Interference Channel (IFC), which is a special case of IBC, has been

MRA技术及临床应用[1]

Lecture 6MR Angiography (MRA) Techniques and ApplicationsChen Lin (林辰)Indiana University School of Medicine & Clarian Health Partners Outline•The background•The principleand techniques•The challengesand solutions•The applicationsHuman Vascular System•Intra cranial•Carotid•Aortic•Coronary•Pulmonary•Abdominal•Renal•PeripheralVascular Abnormities •Stenosis•Aneurysm•Arterial Venous Malformation (AVM)•Thrombus•Plaque•Internal bleeding•…Properties of the Blood•Flow–Velocity: 100 –150 cm/sec in abdominal aorta; 10 –20 cm/sec in peripheral arteries–Pulsatile: Peak arterial flow @ 150 –200 ms afterventricular contraction•T1–~ 1200ms @ 1.5T, ~ 1500ms @ 3T•T2–~ 250ms for arterial blood, ~ 30ms for venous bloodMR Angiography Techniques•Contrast Enhanced MRA (CE‐MRA)–High contrast to noise ratio–No flow induced de‐phasing and signal lost–Fast acquisition ‐> Dynamic imaging–Acquisition timing is important–Gd related NSF is a concern•Non‐Enhanced MRA (NE‐MRA)–Quantitative–Prone to artifacts–Different techniques specific to regionGd Contrast Enhanced MRA•Became popular during 1995‐1999.•Gd contrast agents decrease T 1and increase CNR of blood and soft tissue.•With fast 3D sequences, allow high resolution and coverage of large VOI.•Short acquisition times allow breath ‐holding for visualization of abdominal vasculature.Basic CE ‐MRA Technique•0.1‐0.2 mM/kg (20‐40ml) of Gd contrast injected at 2‐3 ml/sec •Flush with 20‐30ml of saline•3D spoiled gradient echo based sequence •Min. TE and Min. TR0.8 x 0.9 x 0.6 mm 3CE ‐MRA Considerations•Amount of Gd Contrast•Proper acquisition window and timing–Bolus Timing –View Ordering•Improve resolution with partial k ‐space acquisition–Partial Echo, Partial Fourier, Parallel imaging, Radial sampling•Time resolved MRA with view sharing–Key ‐hole, TRICKS/TWIST•Multi ‐station bolus chasing and continuous moving table acquisitionAmount of Contrast•pulmonary arteries 0.1 mmol/kg•aorta0.1 ‐0.2 mmol/kg •renal arteries 0.1 ‐0.2 mmol/kg •portal vein0.2 mmol/kg •peripheral arteries0.3 mmol/kgGd: 20ml Gd: 40mlCourtesy of M. Prince, Cornell, NYArterial and Venous Phases ArteryVeinTime12 sec 18 sec 24 sec 30 sec0 sec CE ‐MRA Acquisition TimingArteryVeinTimePatient Specific DelayRecessed Elliptical Centric View Order•Fluoro Triggering : Realtime 2D scan of ~1 fps)•Test Bolus: 2D fast scan with small doseTime to k ‐space CenterHi ‐res CE ‐MRA of Carotid Artery CE ‐MRADSAAcceleration by Parallel ImagingWithout SENSEWith SENSETime ‐resolved CE ‐MRA (tMRA)ArteryVeinTimetMRA with iPAT=4P.Finn et al., UCLA, Los Angeles, USA1 volume per secHigh Resolution and Large FOV tMRA P.Finn et al., UCLA, Los Angeles, USAMIPAcceleration by Sharing of k ‐space Data•Divide k ‐space into central and peripheral regions.•Sample central k ‐space points more frequently than peripheral points •No lose of SNR.•Increase frame rate, by temporal base remains same (temporal interpolation).TWISTAcceleration by Under ‐sampling T. Gu American Journal of Neuroradiology 26:743‐749VIPR (Vastly under ‐sampled Isotropic PRojection)VIPR Pulmonary tMRA7.5 s 11 s 14.5 s21.5 s 18 s 25 s35.5 s32 s28.5 s11 averaged time frames (7.5 s to 44 s, 37 s total)9150 projectionsT i me 4D (Spatial & Temporal) Information 3D Gd MRA: 87 sec1st station: reverse centric k ‐space acquisition2nd & 3rd stations: centric k ‐space acquisitionBolus Chase peripheral MRA (Run ‐off)Vogt, F. M. et al. Radiology 2007Continuous Moving Table MRA University Munich (LMU)Whole (Body) MRA with Coil ArraysNon‐Enhanced MRA (NE‐MRA)•Time of Flight (TOF)–3D TOF intracranial arterial–2D TOF Carotid and peripheral•Balanced SSFP (bFFE/TrueFISP/FIESTA)–Coronary, Renal•3D Half Fourier FSE–Abdominal, Peripheral•Phase Contrast (PC)Miyazaki, M. et al. Radiology2008;248:20-43Time of Flight (TOF) MRA The Principle of TOFNo Flow Slow Flow Fast FlowMax. SaturationPartial SaturationNo Saturation ImagingSliceThe effective T1is reduced due to in‐flowElimination of Venous Signal Arterial FlowVariable FAExcitation(TONE)Venous FlowTrackingSaturationBandCan be used to identify vessels feeding a given territoryTOF MRA Optimization•Orienting the slice/slab perpendicular to thedirection of flow.•Variation of flip angle across slice profile.•Background tissue suppress with MT.•Fat saturation.•Cardiac gating to reduce pulsatile flow artifact.•Use zero‐fill of k‐space (ZIP) to spatial interpolation.•Acceleration (more applicable at high field)3DTOF MRA with MT at 3.0TDefault 3DTOF3DTOF with ECR MT3DTOF (12:06)3DTOFEC (7:11)3DTOFEC + SENSE(3:41)Accelerated 3D TOF MRA at 3.0T: MT Region2D versus 3D for TOF MRA2D•Less saturation, moresensitive to slow flow•Better contrastbetween blood andstationary tissue3D•Better resolution in slabdirection•More efficientacquisition, greater SNRMultiple Overlapping Thin Slab Acquisition(MOTSA)1.5 T3.0TDSA??The Advantage of High FieldAneurysm (2.8 mm) of the Middle Cerebral ArteryHigher SNR + Longer T1 + MT still possibleFinn, J. P. et al. Radiology 2006;241:338-354Balanced SSFP MRASSFP SequencesG sliceG phaseG readTRααTR−ααG sliceG phaseG readSSFP(FLASH)Balanced SSFP(TrueFISP)Balanced SSFP•T2/T1 Contrast•Flow Compensated•High SNR•Fast Acquisitiono Susceptible to off‐resonance artifactso High SARFinn, J. P. et al. Radiology 2006;241:338-3543D TrueFISP with NAV and FS Finn, J. P. et al. Radiology 2006;241:338-3543D bSSFP of Coronary ArteryLADbSSFP versus CE MRA for Renal ArteryABC DMaki, J. H. et al. Am. J. Roentgenol. 2007;188:W540-W546Navigator Trigger SSFPCE ‐MRAECG FSE MRA (Native)•3D HASTE acquisition in high resolution •Arterial and venous phase separableDr. Vivian Lee et al., NYU, USA,Jian Xu, Siemens, Alto Stemmer, SiemensDelay 1ECGRF EchoIR pulseDelay 2slice 1slice 1Systolic triggering90o 180o 180o 180o 180o 180oDiastolic triggerin gDelay 1Delay 2slice 2slice 2Dual ‐Phase FSE AcquisitionSystolic triggeringDiastolic triggerin gVelocity ContrastDiastolicSystolic‐=Courtesy of LMU, Munich, GermanyNATIVE versus CE for Peripheral MRANative versus CE MRA for Aortic ArterySource ImageMIPPhase Contrast MRA Motion Dependent Phase DifferenceGφBipolar GradientStationary spinsMoving spinsΔφ= γΑτVτAAZY XM XYttRe-phased Magnitude Phase|M1|magnitude of flow compensated signal |M2 –M1|magnitude of signal difference φ2‐φ1phase angle of signal difference flow bright background visibleflow brightbackground suppressedforward flow bright reverse flow black background mid ‐grayPhase Contrast AcquisitionNeed to acquire two images: 1) w/o flow encoding and 2) flow encodedECGAcq WindowAcq WindowEchoess1 = flow compensated (as reference)s2 = flow encodeds1s2s1s2s1s2s1s2s1s2s1s2s1s2s1s2s1s2s1s2Synchronizes with cardiac cycle (typically with retrospective gating)Synchronized and Interleaved Acquisitions for Pulsatile FlowVENC optimalVENC OptimizationVENC too large Poor ContrastVENC too smallAliasing-180+180+200-200+180-180+170-170+180-180+90-90VENC OptimizationPulmonary Artery70-130Aorta100 –175 Carotid Artery80 –120 External Iliac Artery 81 –120 Carotid Syphon55Common Femoral Artery 115Basilar Artery40Superficial Femoral Artery 90Vertebral Artery 40Popliteal Artery 70Sagittal Sinus Vein 10Peripheral Veins5 –10In ‐Plane Sagittal AortaThru ‐Plane Axial AortaVelocity Encoding Direction3D Phase ‐Contrast MRA of Renal CirculationCoronal, Gd enhanced TR/TE = 7/1.4 ms 40o flip, false renal stenosis (False Positive)Coronal, 3D PC TR/TE = 33/6 ms20o flipPC MRA•Requires multiple acquisitions (one reference and one for each flow encoding directions). Long scan time.•Good background suppression from subtraction.•No saturation problem, sensitive for slow flow provided there is adequate SNR and long T2*.•Can be quantitative: Flow velocity ~ φ2 ‐φ1Flow Measurement with PC ‐MRI•Typically uses 2DFT phase contrast method.•Slice positioned perpendicular to axis of vessel.•ROI drawn to delineate vessel lumen–Average value in ROI is mean velocity –Area of ROI is vessel cross ‐sectional area•Flow = Mean velocity * Area.•For pulsatile flow, multi ‐phase cine required.Aorta Measuring Pulsatile Flow with PC MRICSFFinn, J. P. et al. Radiology 2006;241:338-354Normal aortic valveVelocityFlowFinn, J. P. et al. Radiology 2006;241:338-354Aortic Value Stenosis VelocityFlow Summary•Contrast enhanced MRA provides high SNR, however, acquisition timing is critical if time resolved MRA is not used.•More non contrast enhanced MRA have become available. However, they tend to be application specific.•Quantitative measurement of flow velocity and flow rate is possible with PC MRA.Thank You!。

A New Approach to Linear Filtering and Prediction Problems

(2) A nonlinear difference (or differential) equation is derived for the covariance matrix of the optimal estimation error. From the solution of this equation the coefficients of the difference (or differential) equation of the optimal linear filter are obtained without further calculations.
In all these works, the objective is to obtain the specification of a linear dynamic system (Wiener filter) which accomplishes the prediction, separation, or detection of a random signal.4 ———
In his pioneering work, Wiener [1]3 showed that problems (i) and (ii) lead to the so-called Wiener-Hopf integral equation; he also gave a method (spectral factorization) for the solution of this integral equation in the practically important special case of stationary statistics and rational spectra.

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基于非均匀采样的DTMB-A_信号模糊函数抑制方法

第 21 卷第 8 期2023 年 8 月太赫兹科学与电子信息学报Journal of Terahertz Science and Electronic Information TechnologyVol.21，No.8Aug.，2023基于非均匀采样的DTMB-A信号模糊函数抑制方法宋佳乐，万显荣*，张勋，易建新，占伟杰(武汉大学电子信息学院，湖北武汉430072)摘要：新一代数字电视地面广播传输演进标准(DTMB-A)是国标数字电视地面广播信号(DTMB)演进的新一代标准，具有带宽大、抗多径能力强等优点，可作为一种新型的外辐射源雷达机会照射源。

本文阐述了DTMB-A信号模糊函数特性，详细分析了其帧内及帧间模糊副峰的形成机理，分析结果表明DTMB-A信号中确定性重复结构(同步信道、保护间隔等)是造成模糊副峰的主要因素。

对此，提出一种基于非均匀采样的模糊副峰抑制方法。

该方法具有计算复杂度低、易于并行实现等优点。

仿真结果证明所提方法能够将DTMB-A模糊函数修正为理想的图钉型，验证了该方法的有效性，为基于DTMB-A信号的外辐射源雷达目标探测研究提供了方法。

关键词：外辐射源雷达；模糊函数；中国地面数字电视传输标准的演进版本；非均匀采样中图分类号：TN958.97 文献标志码：A doi：10.11805/TKYDA2021102DTMB-A Signal Ambiguity Functions suppression method based onnon-uniform samplingSONG Jiale，WAN Xianrong*，ZHANG Xun，YI Jianxin，ZHAN Weijie(School of Electronic Information，Wuhan University，Wuhan Hubei 430072，China)AbstractAbstract：：Digital terrestrial Television Multimedia Broadcasting-Advanced(DTMB-A), is a new type of illuminator of opportunity for passive radars, which has broad bandwidth and excellentadaptability against multipath effect. In this paper, DTMB-A signal Ambiguity Function(AF) isconcluded and the mechanism of intra-frame and inter-frame ambiguity peaks is researched bytheoretical derivation and simulation verification. The analysis shows that the period deterministic framestructure(the synchronization channel and guard interval) is the main factor that causes the ambiguitysub-peaks. Therefore, a DTMB-A signal ambiguity functions suppression method is proposed by usingnon-uniform sampling, which has low computational complexity and is convenient for parallelcomputing. Simulation results show that this method can suppress DTMB-A signal Ambiguity Functionsinto almost ideal thumbtack shape effectively, which is the foundation of detecting target on DTMB-Apassive radar.KeywordsKeywords：：passive radar；Ambiguity Function；Digital terrestrial Television Multimedia Broadcasting-Advanced(DTMB-A)；non-uniform sampling外辐射源雷达是一种自身不发射电磁信号，利用第三方辐射的机会信号实现目标探测的双/多基地雷达，它具有节约频谱、静默探测、军民两用等诸多优势，备受国内外研究学者关注[1-2]，在传统雷达的研究基础上[3-4]一系列技术均得到了长足发展。

Empirical processes of dependent random variables

2
Preliminaries
n i=1
from R to R. The centered G -indexed empirical process is given by (P n − P )g = 1 n
n
the marginal and empirical distribution functions. Let G be a class of measurabrocesses that have been discussed include linear processes and Gaussian processes; see Dehling and Taqqu (1989) and Cs¨ org˝ o and Mielniczuk (1996) for long and short-range dependent subordinated Gaussian processes and Ho and Hsing (1996) and Wu (2003a) for long-range dependent linear processes. A collection of recent results is presented in Dehling, Mikosch and Sorensen (2002). In that collection Dedecker and Louhichi (2002) made an important generalization of Ossiander’s (1987) result. Here we investigate the empirical central limit problem for dependent random variables from another angle that avoids strong mixing conditions. In particular, we apply a martingale method and establish a weak convergence theory for stationary, causal processes. Our results are comparable with the theory for independent random variables in that the imposed moment conditions are optimal or almost optimal. We show that, if the process is short-range dependent in a certain sense, then the limiting behavior is similar to that of iid random variables in that the limiting distribution is a Gaussian process and the norming √ sequence is n. For long-range dependent linear processes, one needs to apply asymptotic √ expansions to obtain n-norming limit theorems (Section 6.2.2). The paper is structured as follows. In Section 2 we introduce some mathematical preliminaries necessary for the weak convergence theory and illustrate the essence of our approach. Two types of empirical central limit theorems are established. Empirical processes indexed by indicators of left half lines, absolutely continuous functions, and piecewise diﬀerentiable functions are discussed in Sections 3, 4 and 5 respectively. Applications to linear processes and iterated random functions are made in Section 6. Section 7 presents some integral and maximal inequalities that may be of independent interest. Some proofs are given in Sections 8 and 9.

专业英语

J Nondestruct Eval (2010) 29: 63–73DOI 10.1007/s10921-010-0066-4Detection and Advancement Monitoring of Distributed PittingFailure in Gears 齿轮分布式点蚀的探测和先进监控Hasan Ozturk · Isa Yesilyurt ·Mustafa SabuncuPublished online: 24 March 2010© Springer Science+Business Media, LLC 2010Abstract Conventional methods (i.e. time, frequency andcepstrum) can routinely be used to reveal fault-indicatinginformation in the vibration signal. 抽象的常规方法（即时间，频谱，倒谱）能够直接用来揭示振动信号中错误的显示信息。

In recent years, Waveletanalysis, whic h can lead to the clear identiﬁcation of the na-ture of faults, are widely used to describe machine condi-tion.近年来，能够清楚识别自然失效的微波分析被广泛用于描述机器状态。

Capability of this method in the detection of any abnormality can be further improved when its low-order frequency moments are considered. 当考虑低位瞬时频率时，这种能够探测所有异常状况的方法的能力就能够得到进一步提高。

OptiStruct_Optimization

• Shape: is an automated way to modify the structure shape based on a predefined
shape variables to find the optimal shape.
• Size: is an automated way to modify the structure parameters (Thickness, 1D
Copyright © 2008 Altair Engineering, Inc. All rights reserved.
Day 1 Agenda
• • • Introduction Structural Optimization Concepts OptiStruct Features: FEA Solver and Optimizer
•
• • • •
Exercise 5.4: Creating Shapes
Exercise 5.5: Pre-processing the Shape Optimization of a Channel Exercise 5.6: Shape Optimization of a Rail Joint Exercise 5.7: Shape optimization of a 3-D bracket model using Free-Shape method
•
Shape Optimization Concepts (Morphing based and Free Shape)
• •
• • •
Exercise 5.1: Basics of Domains and Handles Exercise 5.2: Morph Volume Exercise 5.3: Mapping a mesh to a new geometry

Turley-TBD-Signal Processing Techniques for Maritime

Signal Processing Techniques for Maritime Surveillance with Skywave RadarMike D.E.TurleyDefence Science and Technology OrganisationEdinburgh,SA,Australiamike.turley@.auAbstract—Detection and tracking of maritime targets using skywave radar is inﬂuenced by the propagation medium,in-terference environment and target scenario.Acquired data dis-play distortion,fading,non-stationarity,and heterogeneity.Brief examples of data are given,then signal processing techniques are developed to provide robust adaptive Doppler processing, rejection of impulsive noise,improved CFAR using the Weibull distribution with robust two-parameter estimation,and a simple track-before-detect scheme for enhancing small SNR target detection performance.I.I NTRODUCTIONSkywave radar presents a means to survey large areas of sea for maritime targets[1].Targets of interest include defense platforms,commercial activities,and illicit incursions and trafﬁcking.Radar operations will typically also monitor associated air movements.In this paper signal processing techniques are presented for improving the radar detection capability of surface targets.The radar cross section of these targets is quite varied and depending on their radial velocity component the targets are to be detected against clutter and/or noise.The performance of a skywave radar ship detection system is highly dependent on the received clutter.Theﬁrst step is thus to select the optimal operating waveform and scan strategy for a given target scenario[2].Once the waveform is selected a number of signal processing objectives may be considered.Ideally the receiver operating characteristic for the complete signal processing detection and tracking system should be optimized.This is however somewhat intractable given the number of available algorithms and parameters,so instead,the focus is on building separable signal processing components that adapt to the target scenario and radar en-vironment.Rudimentarily the signal processing components are range,Doppler,beamforming,peak selection and track-ing.Each of these components are adaptively processed to match signals,counter distortion,and mitigate clutter and interference.In some situations performance may be improved by joint processing.In this paper we do not address spatial processing(see for example[3]).Section II brieﬂy describes the data characteristics,with the following sections describing appropriate temporal,spec-tral,constant false alarm rate(CFAR)and track-before-detect (TBD)signal processing techniques.II.D ATA C HARACTERISTICSFigure1shows two stacked example range-Doppler(RD) maps collected by the Jindalee radar[4]using conventional ta-pered Fourier transform Doppler spectral analysis with0.02Hz analysis bandwidth.The dominant feature is sea backscatter. The sea backscatter appears as positive and negative Doppler shifted lines,known as Bragg lines caused by resonant scat-tering from advancing and receding sea waves of wavelength equal to half the radar carrier wavelength[5].The Bragg lines are smeared due to ionospheric distortion,and have range dependent Doppler shift imposed by ionospheric effects and ocean currents.Second order sea backscatter is present decreasing the potential to detect targets near to and between the Bragg lines.Two target-like responses are observed outside the advance Bragg line.The impulsive nature of a meteoric trail echo produces a Doppler steak at near range.The presence of a second ionospheric propagation path produces a second set of weaker highly range dependent Bragg lines.Detection of targets removed in velocity from the surface backscatter is obviously moretractable.Fig.1.Example RD maps collected using the Jindalee radar. Figure2shows the spectrogram of the range containing the candidate target.The spectrogram spans eleven consec-utive radar scans.Temporal gaps occur between scans.The spectrogram shows the dominant Bragg lines and target signal have associated time varying Doppler shift.The Bragg lines are poorly resolved indicating perhaps additional ionospheric distortion,multipath propagation,or insufﬁcient spatial reso-lution.Parent describes spectral broadening mechanisms[6]. Hence the goal is to detect targets against a clutter and noisebackground that is non-stationary andheterogeneous.Fig.2.Spectrogram of the range containing the candidate target.Eleven consecutive scans are plotted.III.A DAPTIVE D OPPLER P ROCESSING Adaptive Doppler processing is an area of research aimed at coherent processing,mitigating spread Doppler clutter,re-jecting interference,and high resolution processing.Adaptive Doppler processing in HF surface wave radar is described by Fabrizio et al.[7].Doppler processing is segregated here as a temporal pre-conditioning process and as spectral processing.A.Temporal Pre-processingThe main objectives of temporal processing are to reject im-pulsive interference,and do ionospheric distortion correction (IDC).Examples of impulsive noise rejection techniques for skywave radar are given in[8],[9],[10],[11],[12].Other techniques are also suitable,with for example the Sacchi et al.technique[13]developed further here in Section III-B.IDC serves two purposes,namely a pre-processing step to coherent Doppler processing by removing the intra-scan frequency variation[11],[12],[14],[15],and to remove the mean ionospheric Doppler shift thereby improving scan-to-scan Doppler registration prior to subsequent tracking.To register the Doppler shift the full Doppler spectrum was analysed for peak frequency and land or sea classiﬁcation. Smoothing of estimates over nearby range and azimuth cells was employed byﬁtting a low order2D polynomial using a least squares estimate with sample weights based on outlier likelihood.Boundary constraints were included together with a bias towards lower orderﬁtting.A parameter to model ocean current is also necessary when the range-azimuth map spans a land-sea interface.B.Spectral ProcessingHigh resolution spectral analysis methods have the potential to either improve detection of poorly resolved signals,or alter-natively achieve a desired performance with reduced coherent integration time(CIT).The latter incentive has huge impact on radar operations by freeing the precious radar resource for additional scanning.High resolution methods such as auto-regressive(AR)models[16]have been considered,but are sensitive to parameter selection and suffer performance degradation at low SNR.The following subsections describe robust high resolution processing based on parametric and non-parametric data extrapolation techniques.Strictly these are also temporal pre-processing steps to conventional Fourier spectral analysis.1)Parametric Data Extrapolation:Data extrapolation can be used to improve Doppler resolution by extrapolation of the radar sweeps into the past and future.Doppler spectra are characterized by band-limitedness and high dynamic range. These spectra are well suited to robust resolution enhance-ment.Figure3provides a schematic indicating the proﬁt of these techniques.Consider acquisition of N sweeps that with conventional Fourier processing are tapered to reduce sidelobe leakage.The red taper is an N=64sample80dB Taylor example taper.If the sweeps are extrapolated into the past and future,followed by conventional Fourier processing then better use of the acquired data are made.The blue/yellow taper shows the case for an extrapolation factor of2.The blue portion shows increased gain from the acquired data,and the yellow portion shows the extrapolation contributes little energy,but is however critical in controlling the sidelobe leakage from strongsignals.Fig.3.Temporal tapers.Red-taper if only half data was used.Blue-the taper applied to the actual data.Yellow-continuation of the blue taper into the extrapolated sweeps.Data extrapolation methods such as DATEX[17],[18]were introduced to skywave radar application by Turley and V oigt in1992[19].Gadwal and Krolik[20]make comparison of DATEX with other techniques such as Hoppler[21].The DATEX method extrapolates the data ends using linear prediction coefﬁcients.The coefﬁcients are obtained from the full data sequence by assuming a P th order AR model:x n=Pp=1a p x n−p+e n n=1...Nwhere x n are the data samples,a p are stable AR coefﬁcients and e n is a white noise process.Then the linear prediction forward estimates are:ˆx F n=Pp=1a p x n−p n>Nand the backward estimates are:ˆx B n=Pp=1a∗p x n+p n<1Figure4(i)shows a conventionally processed RD map for 128acquired sweeps.This map is considered as‘truth’data. The DATEX method with extrapolation factor2was applied using only the central64sweeps of the original data.The RD map produced using DATEX,Fig.4(ii)is almost identical to the original,indicating a3dB processing gain.Even the broadband signals(impulsive meteoric clutter),for which the AR model is mismatched,are also replicated.This leads to our suggestion to halve the radar scan CIT,and apply the DATEX technique.This frees half the radar resource and thus allows doubling of the scan rate.The technique is robust to model order selection since only the strong clutter signals require modelling and extrapolation.To improve robustness the following modiﬁcations to the DATEX method were made:a)Impulsive noise outlier corruption of the AR coefﬁ-cients was mitigated using the Nuttall[22]technique.b)To account for non-stationary clutter(a potentialproblem for long CITs)the forward and backwardpredictions were based on AR coefﬁcients calculatedfrom respective halves of the acquired data.c)Extrapolation from bad data samples was avoidedusing the minimumﬁlter error technique[19].Fig.4.RD map Doppler processing(i)original‘truth’generated from128 sweeps,(ii)generated from central64sweeps with DATEX extrapolation to128sweeps,(iii)Sacchi extrapolation,and(iv)Sacchi extrapolation& interpolation.2)Non-parametric Data Extrapolation:Non-parametric techniques may also be employed to extrapolate/interpolate the N dimensional time series data x,to form a high resolution K dimensional spectrum s.Our objective is to produce an extended time sequence that is like the original data samples, subject to maximizing the spectral sub-clutter visibility(SCV). SCV is deﬁned as the ratio of total energy to average noise energy.This objective is basically satisﬁed by the Sacchi et al.[13]method where the following cost function is minimized: J(s)=(x E−F s)H P t(x E−F s)+λ2Kk=1ln1+s∗ks k2σ2swhere F is the inverse discrete Fourier transform,H denotes Hermitian transpose,x E is the zero padded extended version of x,λ2is a weighting term with the second term as a regu-larizer imposed by modelling the sparse spectral components as Cauchy distributed.The P t term is introduced here as a temporal selection matrix.The solution is:s=(λ2Q(s)+F H P t F+γ2I)−1F H P t x E(1) whereγ2is introduced to add a small amount of diagonal loading.The diagonal matrix Q is basically a bandlimiting projection that is biased towards the noise spectra:Q kk=1+s∗ks k2σ2s−1with the sparseness controlled by theσs parameter.Note the projection matrix Q is a function of the spectrum s,so the solution is found by calculating(1)iteratively.This method is referred to here as the‘Sacchi’method.The RD map produced using the Sacchi method shown in Fig.4(iii)is also almost identical to the original‘truth’data.The formulation given in (1)allows interpolation or replacement of missing or corrupt sweep data,identiﬁed in P t.Impulsive noise was detected using a clutter whitening AR technique[8];the corrupt sweeps were then replaced using(1)with results shown in Fig.4(iv). Good rejection of the impulsive meteoric clutter was achieved.A similar algorithm based on the l p-norm method[23]offers comparable performance.IV.T WO-P ARAMETER CFARIn skywave radar the receiver operating characteristic de-pends critically on the CFAR algorithm.The RD map data is heterogeneous in range,Doppler and azimuth,and also varies with propagation medium,scattering sources and system gains. The objective of the CFAR algorithm is transform each test cell such that a global threshold will produce a constant false alarm rate.It is desirable that the CFAR transformation has the properties of localization,edge performance,and is robust to target outliers.Due to the heterogeneous nature the scale estimates must be calculated from a small local data sample. The sample support size is a function of range and Doppler, and in particular the support size may be larger in the noise zone than the surface clutter zone.These requirements lead to using order statistic methods[24]with sample kernels matched to typical skywave radar clutter and interference proﬁles[25]. Figure5plots the cumulative distribution function of 146,000samples from several RD maps,one of which is shown in Fig.6(i),on Weibull probability paper.The data values were normalized to a noise energy estimate taken as the 25th percentile.The Weibull probability distribution is given as:p(x)=CBxBC−1exp−xBCx 0where x is the magnitude of the complex data sample,B is a distribution scale parameter,and C is a shape parameter.For the example data of Fig.6(i)the low data values(background noise)follow a shape C=2distribution(Rayleigh),whilst the higher clutter values follow a shape C=0.33distribution. This plot represents the global statistics of the RD maps, and that the local statistics for any test cell may contain contributions from both clutter and noise.To form a CFAR output each test cell x was transformed to a unit Rayleigh distribution using a local shape and scaleparameter:x Rayleigh=xB(x)C(x)2(2)Fig.5.Weibull paper plot of CDF of ARD data.Prior to CFAR processing the data is assumed to be Doppler shift corrected to align the dominant Bragg lines across the range and azimuth dimensions.A.Scale EstimationLocal scale was estimated using order statistics.For range spread clutter,the kernel is declared narrow in Doppler with exclusion of the cell under test.The median of the kernel samples is calculated.The median statistic x med= med{x1...x L}of the L local samples has good properties near clutter edges,is robust to target outliers and also range dependent spread Doppler clutter caused by residual meteor echoes.The median values were then smoothed to form a smoothed estimate x smooth.Theﬁnal scale estimate was taken as a linear combination of the median and smooth estimates:ˆB=w xmed+(1−w)x smooth(3) where the sigmoid like function(0 w 1):w=erf(4log10x med−2)+12provides a convenient methodology to adaptively increase sample size in the more homogeneous noise zones(thereby reducing unnecessary CFAR loss),whilst maintaining edge performance in the high powered clutter zones.As an example,the scale parameter(3)was estimated for the raw data of Fig.6(i),and is shown in Fig.6(ii).Application of the scaling is displayed in Fig.6(iii),showing suppression of clutter and maintenance of a potential target. This process would then be followed by a second stage with a processing kernel aimed at reducing false alarms caused by spread Doppler clutter.It is noted that peak detection may be performed on either the CFAR output(e.g.Fig.6(iii))or the original raw data(e.g.Fig.6(i)).Fig.6.Range scale processing,(i)example RD map post IDC processing, (ii)estimated range scale with kernel,and(iii)scaled RD map.B.Shape EstimationA number of methods have been reported for estimating the shape parameter of a Weibull distribution.Levanon et al.[26]suggest using Dubey’s two-sample order statistics shape estimate[27].For optimal parameter selection the standard deviation in the shape estimate is:ˆσC=0.916LCSo for example the standard deviation is0.43for C=2 and L=20.Unfortunately the estimation variance makes this technique unsuitable for the present small sample sizes,and would cause additional CFAR loss.Alternatively,examination of Fig.5reveals that for this data the Weibull shape parameter is a function of the data scale (at least in a global sense).Let us select a smooth transition function to model the Weibull shape distribution as a function of the estimate scale parameter:C=C hi−C lo2erf4B−B loB hi−B lo−2+1+C lo(4)Such a transform limits the Weibull shape parameter to reasonable values.For example setting C hi=2and B lo= 20dB assumes the data is Rayleigh distributed for scales B<20dB.The transformation curve(4)was estimated by selecting the shape that produced a Rayleigh output,averaged over124radar scans.The CDF of the Fig.5data after Weibull CFAR processing (2)is shown in Fig.7.The data now follows a Rayleighdistribution,apart from a few outliers that are attributed to either target candidates or residual clutter.Figure8(i)shows pre-processed(no signal conditioning) peak history for124radar scans as a function of Doppler. Each pixel represents the maximum over all processed ranges and azimuths for that particular Doppler and scan.The peak spectra are dominated by Bragg clutter.The Bragg lines show the Doppler spectra have a time-varying Doppler shift. Occasional jumps are due to either irregular time steps or radar carrier frequency changes.In the background noise zone targets are masked by broad spectra caused by impulsive noise.Figure8(ii)shows the peak history after processing that included IDC registration,impulse rejection(based on the linear interpolation technique[8])and Weibull CFAR.A number of potential target candidates are observed.Some clutter residuals also exist indicating the CFAR algorithm does not quite produce the desired uniform noise distribution in the Dopplerdimension.Fig.7.Weibull paper plot of CDF of data after CFAR processing.(i)(ii)Fig.8.Peak history as a function of Doppler for(i)pre-processing,and(ii) post IDC,impulse rejection,and CFAR.V.T RACK-B EFORE-D ETECTFor HF radar waveform parameters target structure is unre-solved and candidate detections are typically made by locating waveform ambiguity spreading function peaks.At low SNRs false alarm rates increase and noise introduces a location measurement error.Thus prior to tracking the candidate peak detections are amplitude thresholded,and low SNR targets are lost.A method to improve detection and tracking of the low SNR targets is TBD processing.A host of TBD methods and applications exist.In skywave radar Perlovsky et al.[28] introduced the Maximum Likelihood Adaptive Neural System (MLANS)method for the purpose of aircraft tracking.This iterative method incorporated a function that modeled clutter, targets and noise.As introduced the MLANS method clutter models are not suitable for the ship detection mission.Wallace [29]described a TBD scheme for pulse-Doppler radar based on the Viterbi algorithm with claims of up to10dB SNR gain.Like SIFTER[30]the Wallace method uses the whole volume of radar data,rather than propagating target models. The Wallace method is applied here to skywave radar ship detection.The method processes RD map data to produce a TBD score s based on the previous TBD score,the logarithm of current RD map D,and the supported target models: s(d,r,t)=α·max{s(d m,r m,t−1),D(d,r,t)}+β·D(d,r,t)(5) where r,d,and t are range,Doppler,and scan-time indices respectively,the combination r m,d m denote the m=1...M list of RD cells from which a model target is calculated to arrive from,andβandαcontrol growth and normalization. This scheme has the advantage that the output score dimen-sions are constant,only a single scan history is maintained, and processing load is negligent.Figure9shows conventional thresholded peak detections on a range-time display for a24minute period of actual skywave radar data.The blue dots indicate estimated peak position for each radar scan,the red vectors indicate range prediction based on the measured Doppler,and the vertical green lines indicate the depth of a single range cell.Other examples(not shown)have been observed where the range-rate does not match the Doppler;for these cases it is desired that the TBD scheme defaults to conventional processing. Without a priori knowledge of ionospheric effects the control parameters of(5)were selected to ensure target and false alarm persistence was limited.Figure10shows the peak detections after TBD processing.The peak detection threshold level was set to provide a similar false alarm rate to the conventional output of Fig.9.Initialization and maintenance of potential targets1,2,3&4has improved over conventional processing, and potential false alarms5&6expire as desired.VI.C ONCLUSIONSDetection and tracking of maritime targets using skywave radar is inﬂuenced by the propagation medium,interference environment and target scenario.The data characteristics wereFig.9.Conventionally processed range-time peakdetections.Fig.10.TBD processed range-time peak detections.brieﬂy described,then signal processing techniques were de-veloped to provide robust adaptive Doppler processing,rejec-tion of impulsive noise,improved CFAR using the Weibull distribution with robust two-parameter estimation based only on scale,and a practical track-before-detect scheme for en-hancing small SNR target detection performance.A CKNOWLEDGMENTThe author would like to thank the US ROTHR Program Ofﬁce for the collaborative provision of several of the data sets analyzed and displayed.R EFERENCES[1]J.Headrick and M.Skolnik,“Over-the-horizon radar in the HF band,”Proc.IEEE,vol.62,no.6,pp.664–673,1974.[2]R.Barnes,“Automated propagation advice for OTHR ship detection,”Radar,Sonar and Navigation,IEE Proceedings,vol.143,no.1,pp.53–63,Feb.1996.[3]G.Fabrizio,A.Gershman,and M.Turley,“Robust adaptive beamform-ing for HF surface wave over-the-horizon radar,”IEEE Transactions on Aerospace and Electronic Systems,vol.40,no.2,pp.510–525,Apr.2004.[4] D.Sinnott,“Jindalee–DSTO’s over-the-horizon radar project,”DigestIREECON87,21st International Electronics Convention and Exhibition, Sydney,pp.661–664,Sept.1987.[5] D.Crombie,“Doppler spectrum of sea echo at13.56Mc./s.”Nature,vol.175,pp.681–682,1955.[6]J.Parent,“Statistical study of the spectral broadening of skywavesignals backscattered by the sea surface:Application to RMS wave height measurement with skywave radar,”IEEE Trans.Anten.And Prop., vol.37,no.9,Sept.1989.[7]G.Fabrizio,L.Scharf,A.Farina,and M.Turley,“Robust adaptivedoppler processing for HF surface wave OTH radar,”Proc.Defense Applications of Signal Processing2004.[8]M.Turley,“Impulsive noise rejection in HF radar using a linearprediction technique,”IEEE Conf.on RADAR2003,Sept.2003. [9]M.Turley and herway,“OTHR signal reconstruction for datacorrupted by impulsive noise,”Proc.Radarcon’90,Adelaide,Apr.1990.[10]R.Jarrott,herway,and S.Anderson,“Signal processing for oceansurveillance by HF skywave radar,”IEEE Australian Symposium on Signal Processing and Applications,Adelaide,Apr.1989.[11] herway,G.Ewing,and S.Anderson,“Reduction of some envi-ronmental effects that degrade the performance of HF skywave radars,”IEEE Australian Symposium on Signal Processing and Applications, Adelaide,Apr.1989.[12]Y.Abramovich,S.Anderson,G.Fabrizio,G.Frazer,I.Solomon,andM.Ringer,“Adaptive signal processing techniques and architectures for HF skywave radar,”Proc.Defence Applications of Signal Processing, 2001.[13]M.Sacchi,T.Ulrych,and C.Walker,“Interpolation and extrapolationusing a high-resolution discrete Fourier transform,”IEEE Trans.On Signal Processing,vol.46,no.1,Jan.1998.[14]J.Parent and A.Bourdillon,“A method to correct sky-wave backscat-tered signals for ionospheric frequency modulation,”IEEE Trans.Anten.And Prop.,vol.36,no.1,1988.[15]S.Anderson,“A uniﬁed approach to detection,classiﬁcation,and cor-rection of ionospheric distortion in HF skywave radar systems,”Radio Science,vol.33,no.4,1998.[16]J.Barnum,“Ship detection with high-resolution HF skywave radar,”IEEE Journal of Oceanic Engineering,vol.OE-11,no.2,Apr.1986.[17] D.Swingler and R.Walker,“Linear-predictive extrapolation for narrow-band spectral estimation,”Proc.IEEE,vol.76,no.9,Sept.1988. [18]——,“Line-array beamforming using linear prediction for apertureinterpolation and extrapolation,”IEEE Trans.ASSP,vol.37,pp.16–30,1989.[19]M.Turley and S.V oigt,“The use of a hybrid AR/classical spectralanalysis technique with application to HF radar,”Proc.ISSPA92, International Symposium on Signal Processing and its Applications, Gold Coast,Australia,Aug.1992.[20]V.Gadwal and J.Krolik,“A performance evaluation of autoregressiveclutter mitigation methods for over-the-horizon radar,”Thirty-Seventh Asilomar Conference on Signals,Systems&Computers,vol.1,Nov.2003.[21]G.Frazer,“High–resolution Doppler(Hoppler)processing for skywaveradar,”2001,private communication.[22] A.Nuttall,“Spectral analysis of a univariate process with bad datapoints,via maximum entropy and linear predictive techniques,”Naval Underwater Systems Center,Tech.Doc.5303,New London,CT,1976.[23]M.Cetin,D.Malioutov,and A.Willsky,“A variational technique forsource localization based on a sparse signal reconstruction perspective,”Proc.IEEE ICASSP2002,Orlando,May2002.[24]H.Rohling,“Radar CFAR thresholding in clutter and multiple targetsituations,”IEEE Trans.Aerosp.Electron.Syst.,vol.AES-19,pp.608–621,July1983.[25]M.Turley,“Hybrid CFAR techniques for HF radar,”IEE RADAR97,Oct.1997.[26]N.Levanon and M.Shor,“Order statistics CFAR for Weibull back-ground,”IEE Proceedings,vol.137,Pt.F,no.3,June1990.[27]S.Dubey,“Some percentile estimators for Weibull parameters,”Tech-nometrics,vol.9,pp.119–129,1967.[28]L.Perlovsky,V.Webb,S.Bradley,and C.Hansen,“Improved ROTHRdetection and tracking using the MLANS algorithm,”Studies in proba-bilistic mult-hypothesis tracking and related topics,ed.R.L.Streit,vol.SES-98-01,Feb.1998.[29]W.Wallace,“The use of track-before-detect in pulse-Doppler radar,”Radar2002,pp.315-319,Oct.2002.[30]L.Nickisch,S.Fridman,and M.Hausman,“SIFTER:Signal inversionfor target extraction and registration–coherent processing of IQ data,”Naval Surface Warfare Center Final Technical Report MCR/MRY-R-111, July2003.。

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Optimalencodingofnon-stationarysourcesJohnH.Reifa,JamesA.Storerb,*

aDukeUniversity,USA

bComputerScienceDepartment,BrandeisUniversity,Waltham,MA02254,USA

AbstractTheusualassumptionforproofsoftheoptimalityoflosslessencodingisastationaryergodicsource.Dynamicsourceswithnon-stationaryprobabilitydistributionsoccurinmanypracticalsituationswherethedatasourceisformedfromacompositionofdis-tinctsources,forexample,adocumentwithmultipleauthors,amultimediadocument,orthecompositionofdistinctpacketssentoveracommunicationchannel.Thereisavastliteratureofadaptivemethodsusedtotailorthecompressiontodynamicsources.However,littleisknownaboutoptimalornearoptimalmethodsforlosslesscom-pressionofstringsgeneratedbysourcesthatarenotstationaryergodic.Here,wedonotassumethesourceisstationary.Instead,weassumethatthesourceproducesanin®nitesequenceofconcatenated®nitestringss1...sn,where:

(i)Each®nitestringsiisgeneratedbyasamplingofa(possiblydistinct)stationaryergodicsourceSi,and(ii)thelengthofeachofthesiislowerboundedbyafunctionL󰀁n󰀂suchthat

L󰀁n󰀂=log󰀁n󰀂growsunboundedlywiththelengthnofallthetextwithins1...si.Thuseachinputstringisasequenceofsubstringsgeneratedbypossiblydistinctandunknownstationaryergodicsources.Theoptimalexpectedlengthofacompressedcodingofa®nitepre®xs1...skis

i󰀃1niHi;

whereniisthelengthofsiandHiistheentropyofSi.Wegiveawindow-basedLZ77-typemethodforcompressionthatweprovegivesanencodingwithasymptoticallyoptimalexpectedlength.WegiveanotherLZ77-typemethodforcompressionwheretheexpectedtimeforencodinganddecodingisnearlylinear(approachingarbitrarilyclosetolinearO󰀁n󰀂forlargen).Wealsoprovethatthislatermethodgivesanencodingwith

InformationSciences135(2001)87±105www.elsevier.com/locate/ins

*Correspondingauthor.

E-mailaddress:storer@cs.brandeis.edu(J.A.Storer).

0020-0255/01/$-seefrontmatterÓ2001ElsevierScienceInc.Allrightsreserved.PII:S0020-0255(01)00103-7asymptoticallyoptimalexpectedlength.Inaddition,giveadictionary-basedLZ78-typemethodforcompression,whichtakeslineartimewithsmallconstantfactors.This®nalalgorithmalsogivesanencodingwithasymptoticallyoptimalexpectedlength,assumingtheSiarestationaryergodicsourcesthatsatisfycertainmixingconditionsandL󰀁n󰀂Pn

forsomee>0.Ó2001ElsevierScienceInc.Allrightsreserved.

1.Introduction1.1.ConventionalLZcompressionmethodsThefamilyofLZcompressionmethodsofLempelandZivisaclassoflosslessstringcompressionmethodsthatusevariable-ratecoding.Forsim-plicity,weassumeallinputsourcestobestringsoverthesame®nitealphabetandallcompressedstringstobebinary.TheLZcompressionmethodsareuniversalcodingmethodsinthesensethatthecompressionalgorithmexecuteswithoutinitialknowledgeofthesourcedistribution.Aparsingofastringisadivisionofthestringintosubstringscalledphrases.Adistinctparsingisaparsingsuchthatnotwophrasesareidentical.AnLZcompressionmethodmakesadistinctparsingoftheinputintophrases,andeachofthesephrasesisencodedusinganindexintoadictionarythatcontainspreviouslyparsedphrasesfollowedbyacharacter.Werefertosuchindicesaspointers.Pointersrepresentthematchedportionofthephrase,whichisastringthatmatchestheincominginput.Thecharacterportionofaphraseisthecharacteroftheinputthatfollowsthematchedportion.

1.2.Compressionmethodsusinga®nitewindowInZivandLampel[46],theadaptivedictionaryconsistsofawindowoftheprecedingncharacters,andthematchedportionofthecurrentphraseisthelongestpossiblesubstringwithinthatwindow.

1.3.CompressionmethodsusinganadaptivedictionaryInZivandLampel[47],anadaptivedictionarystorespreviouslyparsedphrases,whichindicateasubstringanywhereinthepreviouscharacters.Thematchedportionofthecurrentphraseisa(longestpossible)phrasestoredinthedictionary.Afterthephraseconsistingoftheindexofthatmatchedportiontogetherwiththenextinputcharacterissenttothedecoder,thatphraseisaddedtothedictionary.Thedecoderreceivesanewphraseconsistingofanindexandacharacter,outputsthatphrase,andaddsthatphrasetothe

88J.H.Reif,J.A.Storer/InformationSciences135(2001)87±105dictionary.Thus,theencoderanddecoderaremaintainingin``lock-step''identicalcopiesofadynamicallygrowingdictionaryofstrings.BothLZ77andLZ78areprovablyoptimalintheinformationtheoreticsenseforergodicsources.Ateachstep,bothLZ77andLZ78sendamatch(aposition-lengthpairofnumbersforLZ77orasingledictionaryindexforLZ78)followedbyanuncompressedcharacter.Theinclusionofarawchar-acterprovidesasimplemechanismtoguaranteethatprogressisalwaysmade(sinceeachphrasemusthavelengthatleastone)andthateachphraseoftheparsingisdistinct(sinceeachphraseisonecharacterlongerthanalongestpossiblematch).Sendingarawcharacterwitheachpointerisusefulforprovingoptimality,butnotnecessary(seeStorerandReif[30]).Inpractice,toavoidtheoverheadofoneuncompressedcharacterforeachmatchedstring,codescanbereservedforthecharactersoftheinputalphabettoguaranteethatallphraseshavelengthatleastone.ThishasnochangeonthematchingprocessofLZ77,andforLZ78onecanstilladdthecurrentmatchtogetherwiththenextinputcharacterasanewdictionaryentry(butthisnextinputcharacterisnotpartofthecurrentphrase),andthedecodercane󰁃ectivelyworkonestepbehindtheencodertodeducethisnextcharacter(e.g.,the``LZW''methodoftheUNIXcompressutilityworksthisway).Wewilldenotemethodsthatre¯ectLZ77operationaswindow-basedorLZ77-typeandmethodsthatre¯ectLZ78operationasdictionary-basedorLZ78-typemethods.ItisalsotypicalinpracticetolimitLZ77-typemethodstoa``win-dow''of®nitelengthandlimitLZ78-typemethodstoadictionaryof®nitesize(byeitherfreezingitwhenitisfullorbyincorporatingadeletionmethod).Anotherpracticalvariationistoallownon-greedyparsing;thatis,useashorterthanpossiblematchatagivensteptoallowforalongermatchatalaterstep.SeethebooksofStorer[29]andBelletal.[3]forapresentationofothervariationsandpracticalimplementations.Forsimplicity,welimitourattentionto``pure''LZ77andLZ78.However,allofthetechniqueswepresentcanbegeneralizedtoincorporatemostpracticalimplementationsofLZ77andLZ78,butifthevariationinquestiongivesupoptimality,thensowillourmethods.