Performance Improvement of GPS_INS Integrated System Using Allan Variance Analysis

Jang Gyu Lee
School of Electrical Engineering And Computer Science, Seoul National University, #034, San 56-1 Shillim-dong, Kwanak-gu, Seoul, 151-744, Korea Tel: +822-872-8190 Fax: +822-878-8198 Email: jgl@snu.ac.kr
2.1 Allan Variance Method
In order to characterise and identify random noise sources of inertial sensors, Allan variance method is employed. The method was initially developed by David Allan of the National Bureau of Standards to quantify the error statistics of a cesium beam frequency standard employed by the U.S. Frequency Standards in the 1960s. In general, the method can be applied to analyse the noise characteristics of any precision measurement instrument. The key attribute of the method is that it allows for a finer, easier characterization and identification of noise sources and their contribution to the overall noise statistics. In this section, an over view of the cluster analysis method is presented and explained the relationship between Allan variance and power spectral density (PSD) distribution of underlying noise sources (Ng and Pines, 1997)(Anon, 1981).
Presenter : Hyunseok Kim ABSTRACT
Because of the sensor noises, the accuracy of a low cost INS deteriorates very rapidly with time. In the INS, deterministic noises of inertial sensors are easing compensated, but random noises must be compensated using a Kalman filter. In this paper, an accurate random noise model of a MEMS inertial sensor is derived to improve the performance of a low cost INS integrated system. The Allan variance analysis method is adopted to model the inertial sensor random noise and then random noise sources are identified and modelled to design the improved model of low cost INS integrated system. If the noise model is accurate, then the navigation performance of newly designed GPS/INS system shows better performance during a GPS blockage. In order to verify the navigation performance of the newly designed GPS/INS system, a car navigation test has been carried out in this research. The test results show a better performance of the improved GPS/INS system model than that of the old GPS/INS system without the Allan variance analysis.
Hyunseok Kim
School of Electrical Engineering And Computer Science, Seoul National University, #034, San 56-1 Shillim-dong, Kwanak-gu, Seoul, 151-744, Korea Tel: +822-872-8190 Fax: +822-878-8198 Email: hyskim77@snu.ac.kr
பைடு நூலகம்
Chan Gook Park
School of Mechanical and Aerospace Engineering, Seoul National University, San 56-1 Shillim-dong, Kwanak-gu, Seoul, 151-744, Korea Tel: +822-880-1675 Fax: +822-888-7958 Email: chanpark@snu.ac.kr
Presented at GNSS 2004 The 2004 International Symposium on GNSS/GPS Sydney, Australia 6–8 December 2004
Performance Improvement of GPS/INS Integrated System Using Allan Variance Analysis
KEYWORDS: Low cost IMU, Allan variance, integrated GPS/INS system
1. INTRODUCTION An Inertial Navigation System (INS) is a system that calculates the position, velocity, and attitude of a vehicle with short-term stability due to the noise characteristics of its inertial sensors. Inertial sensors inherently contain errors in their output. Therefore, the errors must be identified and modelled before they are engaged in an inertial system. The errors of inertial sensors are classified into deterministic error and random error. Deterministic errors are represented by input-output model from dynamics of sensors. On the other hand, random errors of inertial sensors are mixture of several basic errors such as quantiazation noise, white noise, random bias, random walk or random ramp. It is difficult to represent the properties of random errors so that they are properly compensated with filtering technique. The Allan variance method is known to be reliable in identifying a model from a signal of mixed noises. Allan variance is a time domain analysis technique originally developed to study the frequency stability of oscillators. It can be used to determine the character of the underlying random processes that give rise to the data noise. As such, it helps identify the source of a given noise term present in the data. In the Allan variance method of data analysis, the uncertainty in the data is assumed to be generated by noise sources of specific character. The magnitude of each noise source covariance is then estimated from the data. And then, estimated random noise sources are modelled for state variable form using shaping filter technique and applied to Kalman filter of GPS/INS integrated system. The changed inertial sensor model improves the Kalman filter estimation performance. The improvement can be described by the position error reduction of car-navigation result. This paper will study random noise modelling technique and how the random noise models influence on the performance of a low cost GPS/INS integrated system through the car navigation test. The random error sources will be specified which is dominant and newly designed GPS/INS system model that is random error sources are applied will be discussed. 2. RANDOM NOISE ANALYSIS OF INERTIAL SENSORS