针对多传感器观测信息较多、计算效率较低、对动力学模型误差稳键性不佳的问题,提出了一种自适应联邦滤波器并应用于GPS-INS-Odometer组合导航。首先介绍GPS-INS-Odometer组合导航的动力学模型和观测模型,比较分析了信息分配因子和自适应因子的共同特性,论证了联邦滤波器和自适应滤波器的等价性及其等价成立条件,提出了自适应联邦滤波器的信息分配因子构造方法。最后利用实测数据验证了算法的有效性。结果表明,相比于基于GPS和Odometer(里程计)初始方差构造信息分配因子的联邦滤波器,本文提出的自适应联邦滤波器兼容了联邦滤波器高效计算效率,且具有较好的抵抗动力学模型误差效果,能够有效削弱多传感器动力学模型误差对于导航解算的影响,对直接可测参数和间接可测参数的精度提高均起到了积极的作用。
In multi-sensor integrated navigation, extensive observation information, low computational efficiency and weak robust ability will lead to poor navigation performance. An adaptive federated filter is proposed and applied in GPS-INS-Odometer integrated navigation. First the dynamical model and observation model of GPS-INS-Odometer integrated navigation are introduced. Information allocation factor and adaptive factor are compared to find out their common characteristic. The equivalence property between federated filter and adaptive filter is proved and the condition of equivalence is built. The information allocation factor of adaptive federated filter is constructed. Finally an actual calculation was performed to test the validity of new algorithm. The results of the experiment indicate that compared with the information allocation factor constructed by initial variance of GPS and Odometer in classical federated filter, adaptive federated filter shows well robust performance and high computational efficiency. It can weaken the influence of multi-sensor dynamical model disturbance on navigation resolution. The proposed method plays a positive role in improving the accuracy of directly measurable parameters and indirectly measurable parameters.
[1] HASAN A M, SAMSUDIN K, RAMLI A R, et al. A Review of Navigation Systems (Integration and Algorithms)[J]. Australian Journal of Basic and Applied Sciences, 2009, 3(2): 943-959.
[2] HONG S, LEE M H, CHUN H H, et al. Observability of Error States in GPS-INS Integration[J]. IEEE Transactions on Vehicular Technology, 2005, 54(2): 731-743.
[3] SEO J, LEE H K, LEE J G, et al. Lever Arm Compensation for GPS-INS-Odometer Integrated System[J]. International Journal of Control, Automation, and Systems, 2006, 4(2): 247-254.
[4] 肖烜, 王清哲, 程远, 等. 捷联惯导系统-里程计高精度紧组合导航算法[J]. 兵工学报, 2012, 33(4): 395-400. XIAO Xuan, WANG Qingzhe, CHENG Yuan, et al. High Accuracy Navigation Algorithm for Tightly Coupled INS-Odometer[J]. Acta Armamentarii, 2012, 33(4): 395-400.
[5] 张小跃, 杨功流, 张春熹. 捷联惯导-里程计组合导航方法[J]. 北京航空航天大学学报, 2013, 39(7): 922-926. ZHANG Xiaoyue, YANG Gongliu, ZHANG Chunxi. Integrated Navigation Method for SINS and Odometer[J]. Journal of Beijing University of Aeronautics and Astronautics, 2013, 39(7): 922-926.
[6] 严恭敏. 车载自主定位定向系统研究[D]. 西安: 西北工业大学, 2006. YAN Gongmin. Research on Vehicle Autonomous Position and Azimuth Determining System[D]. Xi'an: Northwestern Polytechnical University, 2006.
[7] GEORGY J, KARAMAT T, IQBAL U, et al. Enhanced MEMS-IMU-Odometer-GPS Integration Using Mixture Particle Filter[J]. GPS Solutions, 2011, 15(3): 239-252.
[8] HU Haidong, HUANG Xianlin, LI Mingming, et al. Federated Unscented Particle Filtering Algorithm for SINS-CNS-GPS System[J]. Journal of Central South University of Technology, 2010, 17(4): 778-785.
[9] MAIER A, KIESEL S, TROMMER G F. Performance Analysis of Federated Filter for SAR-TRN-GPS-INS Integration[J]. Gyroscopy and Navigation, 2011, 2(4): 293-300.
[10] GAO Y, KRAKIWSKY E J, ABOUSALEM M A, et al. Comparison and Analysis of Centralized, Decentralized, and Federated Filters[J]. Navigation(Journal of the Institute of Navigation), 1993, 40(1): 69-86.
[11] 顾启泰, 王颂. 联邦滤波器信息分配系数的优选算法[J]. 中国惯性技术学报, 2003, 11(6): 1-6. GU Qitai, WANG Song. Optimized Algorithm for Information-sharing Coefficients of Federated Filter[J]. Journal of Chinese Inertial Technology, 2003, 11(6): 1-6.
[12] YANG Yuanxi, CUI Xianqiang, GAO Weiguang. Adaptive Integrated Navigation for Multi-sensor Adjustment Outputs[J]. Journal of Navigation, 2004, 57(2): 287-295.
[13] LI D, WANG J, BABU S. Enhancing the Performance of Ultra-tight Integration of GPS-PL-INS: A Federated Filter Approach[J]. Journal of Global Positioning Systems, 2006, 5(1-2): 96-104.
[14] 张科, 刘海鹏, 李恒年, 等. SINS-GPS-CNS组合导航联邦滤波算法[J]. 中国惯性技术学报, 2013, 21(2): 226-230. ZHANG Ke, LIU Haipeng, Li Hengnian, et al. SINS-GPS-CNS Integrated Navigation Federal Filtering Algorithm[J]. Journal of Chinese Inertial Technology, 2013, 21(2): 226-230.
[15] TITTERTON H D, WESTON L J. Strapdown Inertial Navigation Technology[M]. 2nd ed. Lexington, Massachusetts: MIT Lincoln Laboratory, 2004.
[16] HAN Songlai, WANG Jinling. Integrated GPS-INS Navigation System with Dual-rate Kalman Filter[J]. GPS Solutions, 2012, 16(3): 389-404.
[17] GODHA S. Performance Evaluation of Low Cost MEMS-based IMU Integrated with GPS for Land Vehicle Navigation Application[D]. Calgary: University of Calgary, 2006.
[18] 吴富梅, 杨元喜. 基于小波阈值消噪自适应滤波的GPS-INS组合导航[J]. 测绘学报, 2007, 36(2): 124-128. WU Fumei, YANG Yuanxi. GPS-INS Integrated Navigation by Adaptive Filtering Based on Wavelet Threshold Denoising[J]. Acta Geodaetica et Cartographica Sinica, 2007, 36(2): 124-128.
[19] 杨元喜. 自适应动态导航定位[M]. 北京: 测绘出版社, 2006. YANG Yuanxi. Adaptive Navigation and Kinematic Positioning[M]. Beijing: Surveying and Mapping Press, 2006.
[20] YANG Yuanxi, GAO Weiguang. An Optimal Adaptive Kalman Filter[J]. Journal of Geodesy, 2006, 80(4): 177-183.
[21] 吴富梅, 杨元喜. 一种两步自适应抗差Kalman滤波在GPS-INS组合导航中的应用[J]. 测绘学报, 2010, 39(5): 522-527. WU Fumei, YANG Yuanxi. A New Two-step Adaptive Robust Kalman Filtering in GPS-INS Integrated Navigation System[J]. Acta Geodaetica et Cartographica Sinica, 2010, 39(5): 522-527.
