Sensor Deployment and Target Localization Based on Virtual Forces

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《复杂背景中的多目标检测与跟踪》范文

《复杂背景中的多目标检测与跟踪》篇一一、引言随着人工智能和计算机视觉技术的快速发展，多目标检测与跟踪在复杂背景下的应用变得尤为重要。

本文将探讨在各种复杂环境中如何有效地实现多目标检测与跟踪的技术方法和研究进展，包括面临的挑战和潜在的应用前景。

二、复杂背景下的多目标检测与跟踪概述多目标检测与跟踪在现实生活中有着广泛的应用，如视频监控、自动驾驶、机器人视觉等。

在复杂背景下，由于场景中目标的多样性和环境的动态性，实现高精度的多目标检测与跟踪是一个极具挑战性的任务。

其主要目的是对场景中的多个目标进行准确的定位、识别和追踪。

三、关键技术与方法在复杂背景下实现多目标检测与跟踪，需要运用一系列先进的技术和方法。

首先，利用深度学习技术，通过训练大量的数据集，使得模型能够自动学习和提取目标的特征信息。

其次，采用目标检测算法，如基于区域的方法和基于回归的方法，对场景中的目标进行准确的定位和识别。

此外，还需要运用多目标跟踪算法，如基于滤波的方法和基于学习的方法，对多个目标进行连续的跟踪和轨迹预测。

四、挑战与解决方案在实现多目标检测与跟踪的过程中，面临着诸多挑战。

首先，由于复杂背景的干扰，如光照变化、阴影、遮挡等，容易导致目标误检和漏检。

针对这一问题，可以通过改进算法的鲁棒性，提高模型对复杂背景的适应能力。

其次，当多个目标相互靠近或重叠时，容易导致目标之间的混淆和跟踪丢失。

为了解决这一问题，可以采用数据关联技术，通过分析目标的特征信息，对不同目标进行准确的区分和匹配。

五、研究进展与实例分析近年来，多目标检测与跟踪技术在研究领域取得了显著的进展。

例如，基于深度学习的目标检测算法在精度和速度上都有了显著的提升。

同时，多目标跟踪算法也在不断优化和改进，如基于全局优化的轨迹预测算法和基于多特征融合的跟踪算法等。

这些技术的进步为多目标检测与跟踪在复杂背景下的应用提供了有力的支持。

以视频监控为例，通过运用多目标检测与跟踪技术，可以实现对场景中多个目标的实时监测和追踪。

sensordeployment

Smart Phone Assisted City-scale Wireless Sensor Network Deployment forTransportation System MonitoringJun Liu,Fiondella Lance,Xu Han,Reda A.Ammar,Sanguthevar Rajasekaran,Nicholas Lownes,John Ivan {jul08003,lﬁondella,xuh08002,reda,rajasek,nlownes,johnivan}@University of Connecticut,Storrs,CT06269,USAAbstract—Ensuring transportation network security is one of the most daunting challenges confronting homeland secu-rity agencies today.Signiﬁcant research has been dedicated to model and analyze the vulnerability of transportation sys-tems,yet notably fewer studies propose speciﬁc strategies for deploying defensive technologies to safeguard these systems. The ultimate goal of situational awareness for prevention and rapid response remains largely unaddressed.Wireless sensors and ad hoc networks are widely regarded as a promising approach to monitor systems and enhance their security.Furthermore,the growth in smart phone usage can contribute additional relay nodes to the network connecting the deployed sensors and command center.Such sensor networks offer the potential to detect a terrorist plot before it can be executed,support effective response to emergency events,and dynamically monitor trafﬁcﬂows to facilitate efﬁcient travel within a city.This paper present a scheme which turn the deployment into a optimization to maximize the weighted coverage,and we demonstrate the approach through the simulation,the result of which clear indicates its effectiveness.I.I NTRODUCTIONA city’s transportation network is a critical infrastruc-ture,which is need to be kept operational for regular economic activities and emergency response,therefore re-quires an approach to effectively monitor the transportation network so that it can provide services despite disruptions. In the literature,there have been a lot research focus on model and analyze the transportation systems[1]–[5], but fewer studies propose speciﬁc strategies for deploying defensive technologies to safeguard these systems.[6], [7]propose idea of using wireless sensor network to monitor the transportation system,however,they are not focus on the deployment of the sensors,or the coverage, connectivity which are very important issues.This paper presents an approach to deploy wireless sen-sor nodes for monitoring a transportation system in order to provide highly reliable data delivery to a homeland security command center within a city.It is assumed that the vulnerability analysis stage has already been carried out and is available to guide deployment according to the relative importance for monitoring portions of the city. The deployed sensor nodes form a network and leverage the additional bandwidth available from smart phones in the city to deliver information to a command center in order to coordinate response services.The existing3G network is also employed in order to ensure the delivery of data.Certain key properties are required,including sensing coverage,network connectivity,and low end to end delay.Connectivity guarantees the availability of a path to deliver information,which includes the communi-cation among deployed sensor nodes,smart phones,and 3G base stations.Aﬂooding-based routing protocol has been designed to allow smart phones to efﬁciently relay sensed information.The end to end delay is the duration between event detection and delivery to the command center,and consists of the sums of access,transmission, propagation,and reception times within each hop.Clearly, the end to end delay should be minimized in order to enable algorithms at the command center to process the data rapidly so that a timely response can be initiated and reﬁned according to the evolving scenario.Relative background information about wireless sensor networks can be found[8],[9].The deployment is formulated as an optimization prob-lem to place sensors at a subset of candidate positions to maximize coverage,guarantee connectivity,and provide fault-tolerance,which will ensure low end to end delay [10].Information from vulnerability analysis,and addi-tional details such as the sensing range of sensor nodes and the mobility patterns of smart phones are incorporated into the utility function of the optimization problem to guide node placement.We illustrate the potential of the approach for applications to detection,trafﬁc monitoring, and travel guidance through a series of simulations of realistic scenarios.Our results demonstrate the approach cost effectively achieves high coverage,guarantees con-nectivity,and delivers fault-tolerant end to end delay. The rest of this paper is organized as follows.Weﬁrst introduce some background knowledge in Section II and describe the scheme in detail in Section III.Following that, we present simulation results in Section IV and offer the conclusion and future work in Section V.II.BACKGROUNDThis section introduce background knowledge relevant to the development of our model,including the branch-and-cut and clustering algorithms.A.Branch-and-cutThe branch-and-cut method is a widely used approach to solve mixed integer programming(MIP)problems.Fig.1illustrates the problem visually The speciﬁcation of thiss.t.Regular constraints:Ax≤b(blue lines in Fig.1),and Integrality constraints:x∈Z+.The feasible regionconsists of9blue dots and x∗∈argminf(x)denotes the optimal solution,which is also an integer point.Inbranch-and-cut,the integrality constraints areﬁrst relaxed,extending the feasible region to the interior of the regionwith blue lines.Cuts valid for all the original feasiblepoints are then generated to tighten the bound of thecontinuous relaxation to obtain the convex hull of theintegral feasible solutions(the red rectangle in Fig.2).The simplex method for linear programs may then beapplied to efﬁciently optimize the relaxed LP problem overthe convex hull.This allows the new problem visualizedin Fig.2to expressed as:min f(x)(2) s.t.Regular constraints:Ax≤b(blue lines in Fig.1), and Facet-deﬁning valid cuts:A′x≤b′(red rectangle in Fig.2).The optimal solution to the revised LP problem given in Eq.(2)is x∗∼∈argmin conv(x)f(x)),which is also the optimal solution to the original problem from Eq.(1). Given the fact thatﬁnding the convex hull is itself an NP hard[]problem,branching operations are often needed so that the problem can be decomposed in to smaller problems that are easier to solve.Hence,we employ the branch-and-bound method to improve the performance of our approach.B.Clustering algorithmThe disjoint-set data structure[11]partitions the de-ployed sensor nodes into disjoint sets based on the trans-mission range“d”and henceforth referred to as“clusters”. In this work,the transmission range is estimated based the assumption of that all the sensor nodes are synchronized, which can be achieved with many existing algorithms like [12]–[15].The algorithm invokes three basic operations of the disjoint-set:MAKE-SET(x),which creates a new set consisting of the single member x such that x does not belong to another set;UNION(x,y)combines the sets containing x and y into a single uniﬁed set;FIND-SET(x)returns a pointer to a unique representative of the set containing x,where all members of a set point to their representative member.The run time of union operations can be reduced sig-niﬁcantly by utilizing the“union by rank”and“path com-pression”heuristics as shown in Algorithm.1.Ref.[11] provides additional details regarding the implementation of these heuristics.III.D ESCRIPTIONThis formulates sensor deployment as an optimization problem and then solving this formulation to achieve the key performance metrics of the applications.We also introduce our cell phone assisted routing scheme here.A.Deployment problem formulation1)Deﬁnition:To capture real-world restrictions on the deployment of sensors in a city such as various forms of private property,our model considers regions where sensors are prohibited.Thus,weﬁrst identify a set of candidate deployment locations beforehand in light of these restrictions.This planning step determines candidate positions,represented by matrix L mn,which indicates the locations of these possible deployment sites throughout the entire transportation system under consideration.Here m is the number of roads,and n the number of points along the m th road.In3G or even4G network,some equipments can serve as access points,providing gateways to connect to existing networks,including the internet.They may also serve as access points to a dedicated network,such as a special monitoring network for the city.Each road possesses a quantitative measure of vulner-ability,which is based on the importance or criticalityDeﬁnitions:1)G:the complete set of sensor nodes2)E[G]:the set of directly connected sensor nodes3)p[x]:parent node of node x4)rank[x]:rank of node xClustering algorithm:1)for each node i∈G2)do MAKE-SET(i)3)for each edge(i,j)∈E[G]4)do if FIND-SET(i)=FIND-SET(j)5)then UNION(i,j)MAKE-SET(x):1)p[x]←−x2)rank[x]←−0UNION(x,y):1)LINK(FIND-SET(x),FIND-SET(y))LINK(x,y):1)if rank[x]>rank[y]2)then p[y]←−x3)else p[x]←−y4)if rank[x]=rank[y]5)then rank[y]←−rank[y]+1FIND-SET(x)with path compression:1)if x=p[x]2)then p[x]←−FIND-SET(p[x])3)return p[x]Algorithm1:Clustering algorithm and disjoint-set oper-ationsof that road.Our previous research[1]offers a game theoretic approach to assess the vulnerability of the links comprising a network.Other risk based methodologies are also possible.Intuitively,a highly vulnerable road should receive priority for coverage by the monitoring network so that problems occurring on this road can be quickly detected and remediated.The present work assumes that each road possess a constant vulnerability given by V m. Future research will explore the possibility of time-varying vulnerability.Because timely response is needed to prevent a prob-lematic situation from worsening,the latency experienced with the network during data delivery is of paramount importance.Many operations incur delays in a single hop of a wireless network,including transmission delay,prop-agation delay,and reception delay,as well as encoding and decoding delay.The sum of these operations is represented by the constantτ.Furthermore,tolerant end to end delay from event detection to command center awareness is denoted byτT.A primary objective is to minimize the overall average end to end delay overhead through3G network.To concretize this formulation,we deﬁne Y as: Y ki={1sensor deployed to point L ki0otherwise(3) 2)The Objective Function:to maximize coverage of the city by sensors,max(n∑k=0m∑i=0V ki Y ki),(4)where V ki denotes the vulnerability of the point L ki. Although the vulnerability of each road is given,some candidate points may be situated at intersections.In these situations,the point vulnerability is computed as the sum of the vulnerabilities of all road crossing a point,capturing the criticality of such crossroads.3)Constraints:are formulated to ensure coverage is achieved.In total,four sets of constraints are considered, including a budget limit for sensors,connectivity require-ments,guarantees of tolerant end to end delayτT.•Budget limit:the budget limit constrains the number of available sensors.We assume the budget is pre-deﬁned and does not change.Thus,it is not possible to add more sensors during the optimization proce-dure,hence we have:n∑k=0m∑i=0Y ki≤N(5)•Connectivity constraint:to guarantee sensor nodes are connected.This basic requirement must be satisﬁed so that detected information can be delivered to the command center.Candidates are clusterized accord-ing to the scheme introduced in Section II given transmission range d.Each cluster consists of a group of connected candidates nodes,but a cluster possesses no connections to sensor nodes belonging to other clusters.To ensure the sensor network deployed can cover the majority of the city,each clusters should have some candidates that are covered by the mon-itoring system.Lettingϕ(υ)representing the cluster υ,we have:∑i∈ϕ(υ)∑Y ki≥1,∀k,υ(6)•Tolerant end to end delay:Tolerant end to end delay guarantees that all events are detected can be deliv-ered to the command center within time no greater thanτT,so that the response can be initiated to prevent the undesirable outcomes that can lead to loss of life and assets.Because this end to end delay is the sum of the delays from the points where the event happens to the access point,and from access point to the command center coverage must be more than the minimum needed to ensure connectivity.Since wired networks are orders of magnitude faster,the delay from access point to the command center treated as aﬁxed constant P a.Thus,the delay depends primarily on the delay from location of the event’s occurrence to an access point.For this reason,each cluster should be able to reach one or more access point,and the overall delay should be less thanτT.Assuming the delay between each pairs of sensor nodes isηij,we have:∑i,j∈ϕ(υ)∑(Y ki×ηij+P a)≤τT,∀k,υ,(7)In above formulation,although the binary variable Y ki=0can always make the equation hold,however, when the position should be selected,Y ki should be “1”.Thanks to the maximization in equation Eq.(4), Y ki tend to be“1”.By solving the optimization problem with branch-and-cut which is introduced in Section II in linear time,the position where the sensors should be deployed can be ﬁgured out.Deploy sensors according to the guideline of the algorithm,the weighted coverage can be maximized.B.Cell phone assisted routingIn this sub-section,we utilize a VBF like[16]routing scheme for mobile smart phones to add additional band-width to the system.Although the original VBF routing protocol couples with broadcast MAC and cannot effec-tively handle collisions,we can apply the geo-routing aware MAC framework in GOAL[17]to VBF.And this would help signiﬁcantly reduce the energy consumption of the sensor nodes.Nowadays,people operate smart phones to talk or text with each other through3G networks as they travel through the city;They also obtained access to internet through a3G or4G network.Thus,smart phones also possess the ability to form an ad hoc network by changing their conﬁgurations for the WIFI.In this mode,smart phones can serve as mobile sensor nodes to aid the mon-itoring system,by helping to deliver some delay tolerant information.However,smart phone users may lack the knowledge needed to reconﬁgure their phones to fulﬁll this task or may with to only allow their phones to assist when they are not being used for other personal purposes. One solution to this problem is to provide registered smart phone user some free services such as real time trafﬁc status in the city,in order incentivize individuals to contribute their smart phone’s computing capabilities for public services.Other strategies to attract contributors is to offer free internet access through the ad hoc network. Here,we assume that an advertising campaign has successfully recruited a large number of smart phones users to register for the service.and that existing an ad hoc network which enables them to connect to access points throughout the majority of the city.Aﬂooding based routing scheme for the mobile smart phones is then employed to deliver information from sensor nodes near events to an access points.Sensor nodes and smart phones know their positions as well as the location of access points.Each packet carries the positions of the sender and the target access points for delivery through the ad hoc network according to a forwarding path speciﬁed by the routing vector from the sender to each target access point.For example,when a smart phone receives a packet,it computes its position relative to the sender and target access point.If the smart phone is close enough to the routing vector(e.g.,less than a predeﬁned distance threshold,denoted as W),it will continue to forward the packet;otherwise,it simply discards the packet.This way, all the smart phones forwarding packets in the ad hoc network form a“routing pipe”.Smart phones in this pipe are eligible for packet forwarding.Those which are not close to the routing vector(i.e.,the axis of the pipe)do not forward.ApSDo not forwardif not in the vectorFig.3illustrates the concept of a routing pipe.Sensor node S0is the source,and A p is the target access point, and the routing vector is determined from their locations. Data packets are forwarded from S0to A p.Forwarders along the routing vector form a routing pipe with a pre-controlled radius(i.e.,the distance threshold).The radius of the routing pipe may be increased to ensure the delivery of critical events.IV.P ERFORMANCE E VALUATIONA.Simulation SettingsThe simulations were developed in CPLEX,which provides an implementation of branch-and-cut to solve the optimization problem.There are100candidates position, and the topology of the candidates position is a10by10 grid of horizontal and vertical roads intersecting at right angles.The intersections(nodes)are labeled1to100,from the left upper node to the node in the lower right.Thevulnerabilities of each horizontal roads is randomly set to 0to1with probability0.5.Furthermore,the are a total of10access points through-out the city,which can be accessed by both wireless sensor nodes and smart phones.These access points are randomly located in the cityAfter the location of the access points are determined,the shortest path to each candidate position is known.Thus,it becomes possible to apply static routing to route information within the wireless sensor network.There are also200registered smart phones randomly distributed in the city,which can help to deliver information.The end to end delay between each pair of smart phones is set to one second,denoted as P s and events occur randomly throughout the city every one hour. The details of the parameter settings for the simulation are given in Table I.TABLE IS IMULATION S ETTINGSSetting Value Setting ValueW80mτT5sd100m P a1sP s1sB.Results and Analysis1)Coverage:Fig.4shows the normalized coverage achieved with different numbers of sensors.Clearly,there is a direct relationship between the number of sensors and the attainable coverage.This agrees with intuition because more sensors can cover more area in the city, ensuring that coverage increases.Theﬁgure also reveals that increasing the number of sensors does not achieve a linear improvement in the coverage.This sublinear im-provement occurs because the optimization scheme covers the most vulnerable portions of the network with greater redundancy to ensure that the command center is notiﬁed of events occurring in critical regions.Less vulnerable roads therefore receive less attention2)Average end to end delay:Fig.5shows the impact of the number of deployed sensors on the average end to end delay.The average end to end delay is always under5seconds,which is the acceptable delay for the detected information to reach the command center.It may also been seen that increasing the number of sensors deployed decreases the average delay.This increase occurs because additional sensor nodes provide more alternative paths to route detected information to access points.Since routing within the wireless sensor network occurs along the shortest paths,these additional paths lower the average end to end delay.3)Average end to end delay with ad hoc:Fig.6 shows the impact of the number of smart phones on the average end to end.Thisﬁgure illustrates that the average end to end delay exceeds5seconds,when the number of smart phones is low.Thus,the number of smartFig.4.Normalized coverage vs.number ofsensorsFig.5.Average delay vs.number of sensorsphone signiﬁcantly inﬂuences the service’s performance. However,when the number of smart phones is large,the average delay is much lower.This trend is similar to Fig.5, which considered the impact of additional sensors.Hence smart phones utilize theﬂooding based routing scheme to provide additional pathes to that deliver data to access points,thereby lowering the end to end delay.4)Average end to end delay with number of access points:Fig.7shows impact of the number of access points on the the average end to end delay.As expected, average delay decreases as the number of access points increases because access points are closer to events within the network and more paths for the detected information to be delivered to an access points.As a result,increasing the number of access points lowers the end to end delay.Fig.6.Average delay vs.number of smart phonesFig.7.Average delay vs.number of access pointsV.C ONCLUSION AND FUTURE WORKThis paper proposes an algorithm to deploy wireless sensor nodes to monitor a transportation system in order to ensure highly reliable data delivery to a homeland security command center within a city.We formulated sensor deployment as an optimization problem to quickly detect events occurring at the most vulnerable portions of the network.We also demonstrated that an ad hoc network formed with smart phones can be harnessed to deliver information to a command center in order to facilitate response.Future research will apply our scheme with real GIS data to evaluate its performance and identify additional constraints to make the approach more practical for adop-in real-world applications.R EFERENCESN.Lownes,Q.Wang,S.Ibrahim,R.Ammar,S.Rajasekaran,andD.Sharma,“A many-to-many game theoretic approach to measur-ing transportation network vulnerability,”Transportation ResearchRecord,vol.2263,pp.1–8,mar2011.S.Tolba,L.Fiondella,R.Ammar,N.Lownes,S.Rajasekaran,J.Ivan,and Q.Wang,“Modeling attacker-technology systeminteraction in transportation networks,”11th IEEE InternationalConference on Technologies for 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trans-portation monitoring network,”Intelligent Control and Automation,WCICA,The Sixth World Congress on2006,vol.2,pp.8640–8644.X.Chen,Y.-A.Kim, B.Wang,W.Wei,Z.J.Shi,and Y.Song,“Fault-tolerant monitor placement for out-of-band wireless sensor network monitoring,”Ad HocNetworks,vol.10,no.1,pp.62–74,2012.[Online].Available:/science/article/pii/S1570870511001223 X.Chen,Y.-A.Kim, B.Wang,Y.Song,H.Dinh,andG.Chen,“Sniffer channel selection for monitoring wireless lans,”Computer Communications,no.0,pp.–,2012.[Online].Available:/science/article/pii/S0140366412001971 J.Liu,X.Han,M.Al-Bzoor,M.Zuba,J.-H.Cui,R.A.Ammar,and S.Rajasekaran,“PADP:Prediction Assisted Dynamic SurfaceGateway Placement for Mobile Underwater Networks,”in TheSeventeenth IEEE Symposium on Computers and Communication(ISCC12),Cappadocia,Turkey,2012.T.Cormen,Introduction to algorithms,ser.MIT electrical engi-neering and computer science series.MIT Press,2001.[Online].Available:/books?id=NLngYyWFl 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无线传感器网络中的移动目标跟踪与感知研究

无线传感器网络中的移动目标跟踪与感知研究无线传感器网络（Wireless Sensor Networks，简称WSNs）是由大量部署在一个空间范围内的低成本、低功耗、小型化的无线传感器节点组成。

这些节点能够感知环境中的各种物理和化学信号，并将这些信息通过网络进行传输和处理，从而实现对环境的实时监测与感知。

在WSNs中，移动目标跟踪与感知一直是一个重要而具有挑战性的研究方向，本文将从不同角度探讨这一问题。

一、无线传感器网络中的移动目标跟踪技术发展随着科技的进步和无线通信技术的发展，无线传感器网络的应用范围不断扩大，涵盖了军事、环境监测、智能交通等众多领域。

然而，在实际应用中，如何准确、高效地跟踪移动目标始终是一个具有挑战性的问题。

1.1 传感器节点选择与部署在无线传感器网络中，传感器节点的选择与部署对于目标跟踪和感知具有重要影响。

传感器节点的选择要能够满足目标检测、定位和追踪的需求，考虑到成本、能量消耗和网络容量等因素。

同时，传感器节点的部署位置也需要经过合理规划，以保证网络的覆盖范围和信号质量。

1.2 目标检测与定位算法目标检测与定位是实现移动目标跟踪的基础，只有准确地检测和定位目标，才能保证后续的跟踪任务的准确性。

常见的目标检测与定位算法包括基于信号强度、时间差测量（Time of Arrival，TOA）和测量的角度等。

这些算法能够通过多节点协同工作，提高目标的定位精度和稳定性。

1.3 目标跟踪算法目标跟踪算法是实现移动目标感知和跟踪的核心技术。

常见的目标跟踪算法包括基于卡尔曼滤波器（Kalman Filter）和粒子滤波器（Particle Filter）的方法。

这些算法能够结合传感器节点的观测值和先验信息，对目标的位置和运动轨迹进行估计和预测。

二、无线传感器网络中的移动目标感知研究移动目标感知不仅包括目标的跟踪，还包括对目标属性和行为的分析。

在无线传感器网络中，如何有效地感知移动目标的属性和行为是一个重要而具有挑战性的问题。

移动机器人论文：基于多传感器信息融合的移动机器人导航定位技术研究

移动机器人论文：基于多传感器信息融合的移动机器人导航定位技术研究【中文摘要】导航定位技术作为移动机器人关键技术之一,是十分热门的研究课题。

特别是未知环境中移动机器人导航定位已经成为移动机器人研究的一个新方向。

移动机器人导航定位需要通过传感器来检测环境的信息,采用单传感器存在很大的局限性,采用多传感器来实现移动机器人定位是必然的。

多传感器信息融合为移动机器人在各种复杂、动态、不确定或未知的环境中工作提供了一种有效的技术解决途径。

本论文以多传感器信息融合技术作为研究重点,结合移动机器人导航定位理论和实践进行探讨,提出了以各种导航定位传感器组合为融合单元,以联合卡尔曼滤波器为融合结构的移动机器人导航定位方法。

论文首先介绍了国内外移动机器人的发展状况、移动机器人的导航定位技术以及多传感器信息融合技术在移动机器人中的应用。

然后详细分析了移动机器人导航定位的基本原理和常用的导航定位方法,并提出了移动机器人导航定位系统的一种新方法。

论文对移动机器人导航定位的传感器和传感器系统进行了分析,重点研究了移动机器人导航定位传感器的信息融合方法,以联合卡尔曼滤波作为融合算法基础,设计了包括惯性导航系统、全球定位系统、里程计、电子罗盘和地图匹配系统在内的多传感器信息融合算法。

论文最后设计制作了一个简化移动机器人系统,在“多传感器数据采集平台”上,进行了移动机器人多传感器信息融合实验和分析,验证了本文提出的技术方法和算法的有效性,可供移动机器人实际研制参考。

【英文摘要】The navigation and localization technology of mobile robot is one of the key technologys, and becoming more and more important. Mobile robot navigation and localization technology in the unknown environment is an emerging robot research direction. The Mobile robot localization needs sensors to detect environmental information, single sensor has limitation and the multiple sensors are needed for robot localization. The integration of multiple sensors provides an effective technical solution for robots’ working in the complex, dynamic, uncertain or unknown environment.The multiple sensors information fusion technology is described in this thesis. The theory and practice of mobile robot localization are combined in the discussion. An information fusion method is proposed for multiple sensors, which fusion unit is the combinations of navigation and localization sensors, and fusion structure is the federated Kalman filter.Firstly, the development and key technology of mobile robot in China and abroad are introduced. The navigation and localization technology and the applications of the multiple sensors information fusion in mobile robot are approached. A new method is also proposed for the mobile robot navigation andlocalization system.The sensor and the sensor system areanalyzed for the mobile robot navigation and localization. The method of data focuses is mainly studied for the mobile robot navigation and localization. A multi data fusion algorithm is designed based on the federated Kalman filter. The multiple sensors system is consisted by inertial navigation system, GPS, odometer, electronic compass and map matching system.Finally,a simplified mobile robot system is designed and made, and the physical experiment of multiple sensors is finished based onthe “Multiple Sensors Data Acquisition Platform”, thevalidity of the algorithm.is verified by simulation andanalysis of measured data.【关键词】移动机器人导航定位多传感器信息融合联合卡尔曼滤波【英文关键词】Mobile Robot Navigation andLocalization Multiple Sensors Information Fusion Federated Kalman Filter【目录】基于多传感器信息融合的移动机器人导航定位技术研究摘要6-7Abstract7第1章绪论11-17 1.1研究背景11-12 1.1.1 移动机器人的发展11-12 1.1.2移动机器人的应用12 1.2 移动机器人导航技术12-13 1.2.1 导航概念12-13 1.2.2 导航关键技术13 1.2.3 移动机器人导航研究意义13 1.3 多传感器信息融合13-16 1.3.1 信息融合技术13-14 1.3.2 机器人技术中的信息融合14 1.3.3 多传感器信息融合的主要方法14-16 1.4 主要研究内容与论文安排16-17第2章导航定位原理与系统17-25 2.1 导航定位原理17-20 2.1.1 机器人模型假设17 2.1.2 机器人位姿表示17-18 2.1.3 机器人运动学模型18-20 2.2 导航定位方法20-22 2.2.1 定位方法分类20-21 2.2.2 常用的定位方式21-22 2.3 导航定位系统实现概述22-24 2.3.1 导航定位系统22-23 2.3.2 导航定位系统实现方法23-24 2.4 本章小结24-25第3章导航定位传感器25-40 3.1 传感器概述25-27 3.1.1 传感器定义25 3.1.2 传感器数学模型25-26 3.1.3 传感器的特性指标26 3.1.4 传感器坐标转换26-27 3.2 传感器分类27-29 3.3 常用的定位传感器29-39 3.3.1 光电编码器29-31 3.3.2 超声波测距传感器31-33 3.3.3 红外测距传感器33-35 3.3.4 电子罗盘35-36 3.3.5 角速率陀螺仪36-37 3.3.6 GPS接收机37-39 3.4 本章小结39-40第4章多传感器信息融合40-56 4.1 信息融合技术概述40-43 4.1.1 信息融合基本概念40 4.1.2 信息融合系统40-41 4.1.3 数据融合常用方法和结构41-42 4.1.4 多传感器信息融合的关键问题42-43 4.2 卡尔曼滤波器43-47 4.2.1 卡尔曼滤波器简介43 4.2.2 卡尔曼滤波器模型43-45 4.2.3 联合卡尔曼滤波器45-47 4.3 多传感器导航定位算法47-54 4.3.1 导航定位多传感器系统47-48 4.3.2 多传感器信息融合方案分析48-49 4.3.3 联合卡尔曼滤波算法设计49-51 4.3.4 子滤波器系统模型51-54 4.4 容错系统设计54-55 4.4.1 故障检测方法54 4.4.2 容错系统54-55 4.5 本章小结55-56第5章实验与结果分析56-65 5.1 移动机器人实验平台56-57 5.2 传感器实验与性能分析57-61 5.2.1 编码器57-58 5.2.2 GPS接收机58-59 5.2.3 电子罗盘59-60 5.2.4 超声波测距传感器60-61 5.2.5 红外测距传感器61 5.3 联合卡尔曼定位实验与分析61-64 5.4 本章小结64-65总结与展望65-67 1 总结65 2 展望65-67致谢67-68参考文献68-72附录1 STM32核心模块电路图72-73附录2 编码器与GPS信息融合仿真程序73-75攻读硕士学位期间发表的论文75。

鸿怀VM821Q1 AMR 4-Pin 方向编码器 IC 说明书

FEATURES• Integrated quadrature sensor IC • Pole size independent operation• 4-pin quadrature, open collector outputs• -40°C to 150°C operating temperature range • Zero speed operation • No calibration required• Insensitive to mechanical vibration• Protection against reverse polarity and short circuit POTENTIAL APPLICATIONS• Industrial speed and direction and position feedback • Encoders• Conveyer rollers speed, process line speed and direction • Gearbox output speed• Positioning roller speed and direction • Garage door opening systems • Induction motors • Fan speed systems• Electric actuated blind position • Pumps and compressors• Integrated seals and bearingsPORTFOLIOThe Honeywell VM821Q1 AMR 4-Pin Quadrature Sensor IC joins the following related products:• VM721D1 AMR 2-Pin PWM Speed and Direction Sensor IC •VM721V1 AMR 2-Pin Speed Sensor ICDESCRIPTIONHoneywell’s Anisotropic Magnetoresistive (AMR) 4-Pin Quadrature Sensor Integrated Circuit (IC) is designed todetect the speed and direction and position of a ring magnet encoder target using a unique* bridge design. The frequency of the output is proportional to the rotational speed of the target, and the rotational direction is encoded by the phase between the outputs. The sensor IC works over a wide range of speeds, temperatures and air gaps.VALUE TO CUSTOMERSThe VM821Q1 sensor IC has a higher sensitivity AMR bridge array that operates with a larger airgap than Hall-effect sensor ICs, which allows for enhanced design flexibility and assembly tolerances. The sensor IC has been optimized to provide an output that is not affected by target runout or sudden air gap changes. It is insensitive to magnet pole size, allowing one sensor to be paired with different ring magnet applications.DIFFERENTIATIONHoneywell’s unique solution utilizes the AMR bridge in saturation, which provides a more stable output response when the system has vibration, sudden air gap changes, or target runout without requiring complex magnitude compensation algorithms. The AMR signal has greatersensitivity than Hall-effect sensor ICs, and does not require automatic gain control or chopper stabilization that can lead to increased jitter over the operating range. *Patent PendingAMR 4-Pin Quadrature Sensor Integrated CircuitVM821Q132336294Issue E23Advanced Sensing TechnlologiesAdvanced Sensing Technologies AMR 4-Pin Quadrature Sensor ICVM821Q1AMR 4-Pin Quadrature Sensor ICVM821Q1Figure 1. Block DiagramNOTICEAbsolute maximum ratings are the extreme limits the device will momentarily withstand without damage to the device. Electrical and mechanical characteristics are not guaranteed if the rated voltage and/or currents are exceeded, nor will the device necessarily operate at absolute maximum ratings.Phase Calculation DefinitionThis method isolates phase from duty cycle. It also best correlates to analysis of the fundamental frequency in the frequency domain.Where:A rising = rising edge of output A A falling = falling edge of output AB rising = nearest falling edge of output B to A rising B falling = next falling edge of output BT = period of one cycleB rising + B falling2Phase (°) =A rising + A falling2-*((360TNOTICELarge, stray magnetic fields in the vicinity of the sensor may adversely affect sensor performance.For more informationHoneywell Advanced SensingTechnologies services its customers through a worldwide network of sales offices and distributors. For application assistance, current specifications, pricing or the nearest AuthorizedDistributor, visit /ast or call:Asia Pacific +65 6355-2828Europe +44 (0) 1698 481481USA/Canada +1-800-537-6945Honeywell Advanced Sensing Technlogies830 East Arapaho Road Richardson, TX /ast4Advanced Sensing TechnlologiesAMR 4-Pin Quadrature Sensor ICVM821Q1Figure 5. Dimensions and Product Marking (For reference only mm/[in])0,80Date code (one digit: 1-9)ADDITIONAL INFORMATIONThe following associated literature is available on the Honeywell web site at :• Installation instructions• Application notes • Technical notes • CAD Models• Evaluation samples available from your local Honeywell contact32336294-E-EN | E | 05/21© 2021 Honeywell International Inc.Product MarkingFigure 4. Sensor IC Mounting OrientationRadialAxialWarranty/RemedyHoneywell warrants goods of its manufacture as being free of defective materials and faulty workmanship during theapplicable warranty period. Honeywell’s standard product warranty applies unless agreed to otherwise by Honeywell in writing; please refer to your order acknowledgment or consult your local sales office for specific warranty details. If warranted goods are returned to Honeywell during the period of coverage, Honeywell will repair or replace, at itsoption, without charge those items that Honeywell, in its sole discretion, finds defective. The foregoing is buyer’s sole remedy and is in lieu of all other warranties, expressed or implied, including those of merchantability and fitness for a particular purpose. In no event shall Honeywell be liable for consequential, special, or indirect damages.While Honeywell may provide application assistancepersonally, through our literature and the Honeywell web site, it is buyer’s sole responsibility to determine the suitability of the product in the application.Specifications may change without notice. The information we supply is believed to be accurate and reliable as of this writing. However, Honeywell assumes no responsibility for its use.。

sensons作文

sensons作文Sensons is a revolutionary technology that has the potential to change the way we interact with the world around us.Sensons是一种革命性技术，可能会改变我们与周围世界互动的方式。

By combining sensors and artificial intelligence, Sensons can gather data from our environment and provide us with valuable insights and information.通过结合传感器和人工智能，Sensons可以收集我们环境的数据，并为我们提供有价值的见解和信息。

One of the most exciting applications of Sensons is in the field of healthcare, where it can be used to monitor patients' vital signs and detect any potential health problems early on.Sensons在医疗保健领域的一个最令人兴奋的应用是，它可以用来监测患者的生命体征，并及早发现任何潜在的健康问题。

In addition to healthcare, Sensons can also be used in smart homes to make our lives more convenient and efficient.除了医疗保健，Sensons还可以用于智能家居，使我们的生活更加便利和高效。

For example, Sensons can be used to control the temperature and lighting in our homes, as well as to monitor energy usage and optimize efficiency.例如，Sensons可以用来控制我们家中的温度和照明，以及监测能源使用情况并优化效率。

CTU 开发套件用户指南说明书

DescriptionCambridgeIC’s Central Tracking Unit (CTU) is a single chip processor for sensing linear and rotary position. CTU chips work with resonant inductive position sensors. These are manufactured with standard PCB technology. This means sensors are stable, robust and cost effective. Sensors are available in a number of measuring lengths and configurations.Sensors work with contactless targets that comprise an electrical resonator sealed inside a precision housing. CambridgeIC’s standard target is manufactured by Epcos AG, Europe’s leading supplier of passive components.The CTU Development Kit includes all of the parts needed to get a CTU position sensing system working. It includes a USB interface and software for a PC, for demonstration and evaluation. Alternatively, theCAM204 chip’s interfaces are available on a 14-pin IDC connector. This enables the system to be interfaced with the customer’s own host system during later development. Kit Features•CTU Development Board (CAM204 chip)• 4 x Type 1 linear sensors from 25mm to 200mm • 3 x Type 1 360° rotary sensors• 4 x Targets•CTU Adapter for SPI to USB conversion•PC software for Windows XP/Vista•Ready to work inside the box Applications•Demonstration•Evaluation•Development•One-off position sensing solutionsProduct identificationPart no. Description013-7002 CTUDevelopmentKitFigure 1 CTU Development Kit1Quick Start Guide1.1Start with Kit Contents in the BoxThe CTU Development Kit is designed to work inside the box for preliminary demonstration and evaluation. Only the CD and the PC end of the USB cable need be removed. The CTU Development Board is already connected to the sensors and to the CTU Adapter. Sensors are clipped onto the underside of a clear plastic tray, which also acts as guide rails for aligning targets correctly with sensors.Once the system is working, with the positions of all 4 targets displayed on a PC, parts can be removed for further evaluation and integration.1.2Plug the USB Cable into a PCThe software provided is for Windows XP and Vista. Turn the PC on and plug the USB cable into a convenient port. The Windows Found New Hardware Wizard should launch.1.3Install the Windows Driver for the CTU AdapterThe driver files are on the CD provided. Copy these to a convenient file location on the PC. They may be required later if the Adapter is subsequently connected to a different USB port.In the Windows Found New hardware Wizard, select No, not this time, then click on Next. Tell Windows where to look for the driver files just copied to the PC, and press Next. Windows will now issue a compatibility warning. Press Continue Anyway. After a few seconds the wizard should complete successfully. Press Finish to complete half of the driver installation, and repeat the process a second time to load both parts of the driver.Full details, including screenshots and how to verify installation, are in the CTU Development Applications User Guide.1.4Install the CambridgeIC CTU SoftwareThe CTU Development Applications are on the CD provided. Save these files to an appropriate directory on the target PC. It is recommended to shut all other programs before installation.Locate and launch the setup.exe program from the directory containing the installer. Follow the on-screen prompts to complete the installation. Once completed, the applications require a restart of the PC for correct operation.1.5Launch CTU DemoFrom the PC’s start menu, select All ProgramsÆCambridgeIC CTU SoftwareÆCtuDemo. CTU Demo should run and display the positions of each sensor’s target. Targets are supplied with holders in a bag under the CD. Please see section 2 for how to align them with linear and rotary sensors.For full details of CTU Demo and the other applications provided please refer to the CTU Development Applications User Guide. This also includes a troubleshooting guide in case of difficulties.1.6Scaling Reported Position to Physical UnitsThe CTU Development Applications can display reported position in physical units (mm or degrees). This requires the correct value of the Sin Length parameter to be entered. Free space values are listed below for convenience. Please refer to the sensor’s datasheet for other conditions.Assembled sensor part number Configuration MeasuringLengthNominalTarget GapSin Length013-0006 Rotary 360°1.5mm 360°013-0007 Linear 25mm 37.9mm 013-0008 Linear 50mm 63.0mm 013-0009 Linear 100mm 113.2mm 013-0010 Linear 200mm 213.1mm2Aligning Targets and SensorsThe CTU Development Kit is supplied with 4 targets and holders for the rotary and linear sensors.For best performance, sensors and their targets should be aligned as shown in Figure 1 and Figure 2. Dimensions are in mm. The clear plastic tray maintains a minimum gap of approximately 1mm between the sensor and target. The system will function with gap up to 5mm (an additional 4mm), although resolution will decrease.target holder forlinear sensorsFigure 2 target alignment with linear sensorstarget holder forrotary sensorsPlease refer to sensor datasheets for detailed performance and alignment data.3CTU and Adapter Firmware UpdatesThe CTU Development Applications include UpdateCtuFirmware and UpdateAdapterFirmware which can be used to load new CTU or Adapter firmware files (.cff or .aff) respectively.4PrecautionsTargets are a push fit in the holders supplied with the CTU Development Kit. These holders are not designed for high speed operation. Targets may vibrate free and cause injury.5Next StepsOnce the system’s function has been verified in the CTU Development Kit’s box…•Parts can be removed and evaluated using a customer’s test equipment.•Sensors and targets can be integrated with a customer’s own product.• A customer can develop their own PC applications that communicate with the CTU through the Adapter using…o LabVIEW, based on CambridgeIC example VIs, oro Another .NET programming language, using CambridgeIC’s Class Library and VB sample code.•The CTU Development Board can be connected to the processor of an end product prototype, so that the processor can communicate with the CTU chip over its SPI interface.•The CTU chip can be designed into the product itself.If none of the sensors provided in the CTU Development Kit are appropriate for the end application, please contact CambridgeIC to discuss alternatives.6Kit ContentsThe table below lists the contents of the CTU Development Kit. There first column is the part number for the hardware (if available separately), and the second is the part number of the datasheet (where applicable). Electronic copies of the datasheet are on the CD.Hardware part no Datasheetpart noQty Description013-6003 1 CD with software and documentation013-5006 033-0010 1 CTU Development Board including CAM204 CTU chip013-7001 033-00141 CTUAdapter013-0006 033-0002 1 360° 25mm diameter rotary Type 1 sensor assembly013-0007 033-0004 1 25mm linear Type 1 sensor assembly013-0008 033-0004 1 50mm linear Type 1 sensor assembly013-0009 033-0004 1 100mm linear Type 1 sensor assembly013-0010 033-0004 1 200mm linear Type 1 sensor assembly013-0011 033-0015 1 360° 50mm diameter rotary Type 1 sensor assembly013-0012 033-0016 1 360° 36mm diameter rotary Type 1 sensor assembly013-6001 4 300mm 6-way sensor connecting cable013-6002 1 60mm 14-way SPI interface connecting cable033-0009 033-0009 1 Print-out of the CTU Development Kit User Guide013-1005 033-0005 4 Standard targetsTarget holders, 3 linear and 1 rotaryThe table below lists the contents of the CD supplied with the CTU Development Kit. The CD also includes datasheets for the items listed above. Please contact CambridgeIC for the latest versions.Part number Description021-0001 Windows Adapter Driver021-0002 CambridgeIC.DLL Class Library for communication with a CTU through the Adapter022-0003 CTU LabVIEW VIs023-0001 Visual Basic Sample Code026-0001 CambridgeIC CTU Software Installer033-0003 Datasheet for CAM204 CTU chip033-0006 Class Library User Guide033-0007 CambridgeIC CTU Software User Guide033-0008 CTU LabVIEW VI User Guide033-0012 Resonant Inductive Operating Principle033-0013 End Shaft Sensor Operating Principle7Document HistoryRevision Date ReasonA 24 August 2009 First draft0002 5 November 2009 Added 50mm sensor to hardwareAdded Visual Basic Sample Code to softwareAdded Windows Adapter Driver0003 23 November 2009 Updated Kit Features with extra sensorUpdated introduction0004 4 February 2010 Updated logo and styleAdded further documents to list of CD contents0005 23 July 2010 Updated based on CAM204BE and new sensors0006 16 July 2011 Illustrated new linear target holder designUpdated with additional sensorsReformatted Kit Contents section8Contact InformationCambridge Integrated Circuits Ltd21 Sedley Taylor RoadCambridgeCB2 8PWUKTel: +44 (0) 1223 413500********************9LegalThis document is © 2009-2011 Cambridge Integrated Circuits Ltd (CambridgeIC). It may not be reproduced, in whole or part, either in written or electronic form, without the consent of CambridgeIC. This document is subject to change without notice. It, and the products described in it (“Products”), are supplied on an as-is basis, and no warranty as to their suitability for any particular purpose is either made or implied. CambridgeIC will not accept any claim for damages as a result of the failure of the Products. The Products are not intended for use in medical applications, or other applications where their failure might reasonably be expected to result in personal injury. The publication of this document does not imply any license to use patents or other intellectual property rights.。

simultaneous localization意思

simultaneous localization意思
"Simultaneous Localization and Mapping"（SLAM）是一种技术，用于使机器人或无人驾驶车辆在未知环境中同时进行自身位置估计和地图构建。

SLAM 的目标是通过传感器数据（如摄像头、激光雷达、惯性测量单元等）来实时地确定机器人相对于其周围环境的位置，并在此过程中构建环境地图。

具体而言，"Simultaneous Localization" 意味着机器人正在不断地估计自己的位置，尽管它可能不知道环境的确切地图。

"Mapping" 意味着机器人在同时定位的过程中，使用传感器数据创建环境的地图。

SLAM 技术广泛应用于无人驾驶车辆、无人机、机器人和虚拟/增强现实等领域。

这项技术的成功应用对于实现自主导航和感知是至关重要的。

SLAM 系统需要处理传感器数据的噪声、误差，以及环境中的动态变化等问题，因此它通常结合了机器学习、计算机视觉和传感器融合等技术。

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We present the virtual force algorithm (VFA) as a sensor deployment strategy to enhance the coverage after an initial random placement of sensors. The VFA algorithm is inspired by disk packing theory [11] and the virtual force ﬁeld concept from robotics [5]. For a given number of sensors, VFA attempts to maximize the sensor ﬁeld coverage using a combination of attractive and repulsive forces. During the execution of the force-directed VFA algorithm, sensors do not physically move but a sequence of virtual motion paths is determined for the randomly-placed sensors. Once the effective sensor positions are identiﬁed, a one-time movement is carried out to redeploy the sensors at these positions. Energy constraints are also included in the sensor repositioning algorithm. We also propose a novel target localization approach based on a two-step communication protocol between the cluster head and the sensors within the cluster. In the ﬁrst step, sensors detecting a target report the event to the cluster head. The amount of information transmitted to the cluster head is limited; in order to save power and bandwidth, the sensor only reports the presence of a target, and it does not transmit detailed information such as signal strength, conﬁdence level in the detection, imagery or time series data. Based on the information received from the sensor and the knowledge of the sensor deployment within the cluster, the cluster head executes a probabilistic scoring-based localization algorithm to determine likely position of the target. The cluster head subsequently queries a subset of sensors that are in the vicinity of these likely target positions. The sensor ﬁeld is represented by a two-dimensional grid. The dimensions of the grid provide a measure of the sensor ﬁeld. The granularity of the grid, i.e. distance between grid points can be adjusted to trade off computation time of the VFA algorithm with the effectiveness of the coverage measure. The detection by each sensor is modeled as a circle on the two-dimensional grid. The center of the circle denotes the sensor while the radius denotes the detection range of the sensor. We ﬁrst consider a binary detection model in which a target is detected (not detected) with complete certainty by the sensor if a target is inside (outside) its circle. The binary model facilitates the understanding of the VFA model. We then investigate a realistic probabilistic model in which the probability that the sensor detects a target depends on the relative position of the target within the circle. The details of the probabilistic model are presented in Section III. The organization of the paper is as follows. In Section II, we review prior research on topics related to sensor deployment in DSNs. In Section III, we present details of the VFA algorithm. In Section IV, we present the target localiz Distributed sensor networks (DSNs) are important for a number of strategic applications such as coordinated target detection, surveillance, and localization. The effectiveness of DSNs is determined to a large extent by the coverage provided by the sensor deployment. The positioning of sensors affects coverage, communication cost, and resource management. In this paper, we focus on sensor placement strategies that maximize the coverage for a given number of sensors within a cluster in cluster-based DSNs. As an initial deployment step, a random placement of sensors in the target area (sensor ﬁeld) is often desirable, especially if no a priori knowledge of the terrain is available. Random deployment is also practical in military applications, where DSNs are initially established by dropping or throwing sensors into the sensor ﬁeld. However, random deployment does not always lead to effective coverage, especially if the sensors are overly clustered and there is a small concentration of sensors in certain parts of the sensor ﬁeld. The key idea of this paper is that the coverage provided by a random deployment can be improved using a force-directed algorithm.
Sensor Deployment and Target Localization Based on Virtual Forces
Yi Zou and Krishnendu Chakrabarty
Abstract— The effectiveness of cluster-based distributed sensor networks depends to a large extent on the coverage provided by the sensor deployment. We propose a virtual force algorithm (VFA) as a sensor deployment strategy to enhance the coverage after an initial random placement of sensors. For a given number of sensors, the VFA algorithm attempts to maximize the sensor ﬁeld coverage. A judicious combination of attractive and repulsive forces is used to determine virtual motion paths and the rate of movement for the randomly-placed sensors. Once the effective sensor positions are identiﬁed, a one-time movement with energy consideration incorporated is carried out, i.e., the sensors are redeployed to these positions. We also propose a novel probabilistic target localization algorithm that is executed by the cluster head. The localization results are used by the cluster head to query only a few sensors (out of those that report the presence of a target) for more detailed information. Simulation results are presented to demonstrate the effectiveness of the proposed approach. Index Terms— Sensor coverage, distributed sensor networks, sensor placement, virtual force, localization.