Detection of Bias in GPS Satellites' Measurements: A Probability Ratio Test Formulation

被引:23
作者
Abdel-Hafez, Mamoun F. [1 ]
机构
[1] Amer Univ Sharjah, Dept Mech Engn, Sharjah 26666, U Arab Emirates
关键词
Bias estimation; fault detection and identification (FDI); global positioning system (GPS); inertial navigation sensor; Kalman filtering (KF); HIGH-GAIN OBSERVER; SYSTEM;
D O I
10.1109/TCST.2013.2267093
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A sequential and multihypothesis probability ratio test is proposed for detecting and identifying a bias fault in GPS pseudorange measurements. Initially, a measurement residual variable that is only a function of the measurement noise and the possible bias fault is constructed. The probability of this residual given a certain bias hypothesis is then obtained. Subsequently, an error variable is constructed for each hypothesis based on the ratio of the probability of that hypothesis to the probability of a base hypothesis. The propagation of the error variables with time is monitored for all hypotheses. If a hypothesis is associated with the true bias on the satellite measurement, then the corresponding error variable will remain around zero in mean. Otherwise, in case of a wrong hypothesis, the associated error variable will diverge away from zero. Error bounds for declaring false hypotheses are formulated in this brief. The advantage of the proposed method is that false hypotheses are continuously removed from the hypothesis set when their error variables exceed the error bound. Therefore, the size of the hypothesis set will reduce with time, ending up with only the correct bias hypothesis. This will result in a monotonic reduction in the computational time of the method. Finally, an ultratightly coupled filter structure is used to test the performance of the proposed method and the obtained results will be presented.
引用
收藏
页码:1166 / 1173
页数:8
相关论文
共 18 条
  • [1] Abdel-Hafez M. F., 2011, INT J AEROSP MECH EN, V5, P233
  • [2] The Autocovariance Least-Squares Technique for GPS Measurement Noise Estimation
    Abdel-Hafez, Mamoun F.
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2010, 59 (02) : 574 - 588
  • [3] Bar-Shalom Y., 2004, Estimation with applications to tracking and navigation: Theory algorithms and software
  • [4] A SEQUENTIAL PROCEDURE FOR MULTIHYPOTHESIS TESTING
    BAUM, CW
    VEERAVALLI, VV
    [J]. IEEE TRANSACTIONS ON INFORMATION THEORY, 1994, 40 (06) : 1994 - 2007
  • [5] Chan S., 2004, P 17 INT TECHN M SAT, P1798
  • [6] High-Gain Observer-Based Estimation of Parameter Variations With Delay Alignment
    Dai, Xuewu
    Gao, Zhiwei
    Breikin, Timofei
    Wang, Hong
    [J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2012, 57 (03) : 726 - 732
  • [7] Multihypothesis sequential probability ratio tests - Part II: Accurate asymptotic expansions for the expected sample size
    Dragalin, VP
    Tartakovsky, AG
    Veeravalli, VV
    [J]. IEEE TRANSACTIONS ON INFORMATION THEORY, 2000, 46 (04) : 1366 - 1383
  • [8] Multihypothesis sequential probability ratio tests - Part I: Asymptotic optimality
    Dragalin, VP
    Tartakovsky, AG
    Veeravalli, VV
    [J]. IEEE TRANSACTIONS ON INFORMATION THEORY, 1999, 45 (07) : 2448 - 2461
  • [9] Self-Tuning Multisensor Weighted Measurement Fusion Kalman Filter
    Gao, Yuan
    Jia, Wen-Jing
    Sun, Xiao-Jun
    Deng, Zi-Li
    [J]. IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2009, 45 (01) : 179 - 191
  • [10] Novel Parameter Identification by Using a High-Gain Observer With Application to a Gas Turbine Engine
    Gao, Zhiwei
    Dai, Xuewu
    Breikin, Tim
    Wang, Hong
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2008, 4 (04) : 271 - 279