新息向量的抗差Kalman滤波方法及其在UWB室内导航中的应用
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摘要
针对超宽带(ultra wideband,UWB)室内导航中非视距(non line of sight,NLOS)测距误差会大幅降低导航精度以及系统噪声的不确定性导致滤波精度不高的问题,提出了一种基于新息向量的抗差Kalman滤波方法。该方法在UWB室内导航线性化模型的基础上,利用单个新息值构造抗差因子矩阵,从而消除非视距测距误差的影响,同时对系统噪声协方差矩阵进行实时估计和修正。实验结果表明,该方法不但能有效地消除非视距测距误差对导航解算的影响,而且能进一步提高导航解算的精度和稳定性。
In UWB indoor navigation, the accuracy of navigation resolution is greatly affected by non line of sight(NLOS) ranging error, and low filtering precision is influenced by uncertain system noise. To solve these problems, an improved robust Kalman filtering based on innovation is proposed and applied in ultra wideband(UWB) indoor navigation. On the foundation of the linear UWB indoor navigation model, the new method uses single innovation values to construct the matrix with robust factors and eliminate the influence of NLOS ranging error. Meanwhile, the new method does real-time estimation and corrects the system noise covariance matrix. Experimental result verifies the effectiveness of the new method. It is shown that the new method can not only effectively eliminate the influence of NLOS ranging error on navigation resolution, but also can further improve the filter precision and reliability in indoor navigation.
In UWB indoor navigation, the accuracy of navigation resolution is greatly affected by non line of sight(NLOS) ranging error, and low filtering precision is influenced by uncertain system noise. To solve these problems, an improved robust Kalman filtering based on innovation is proposed and applied in ultra wideband(UWB) indoor navigation. On the foundation of the linear UWB indoor navigation model, the new method uses single innovation values to construct the matrix with robust factors and eliminate the influence of NLOS ranging error. Meanwhile, the new method does real-time estimation and corrects the system noise covariance matrix. Experimental result verifies the effectiveness of the new method. It is shown that the new method can not only effectively eliminate the influence of NLOS ranging error on navigation resolution, but also can further improve the filter precision and reliability in indoor navigation.
引文
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[14] Tan Xinglong,Wang Jian,Han Houzeng. SVR Aided Adaptive Robust Filtering Algorithm for GPS/INS Integrated Navigation[J].Acta Geodaetica et Cartographica Sinica,2014,43(6):590-597(谭兴龙,王坚,韩厚增. 支持向量回归辅助的GPS/INS组合导航抗差自适应算法[J]. 测绘学报,2014,43(6):590-597)
[15] Miao Yuewang,Zhou Wei,Tian Liang,et al. Extended Robust Kalman Filter Based on Innovation Chi-Square Test Algorithm and Its Application[J]. Geomatics and Information Science of Wuhan University,2016,41(2):269-273(苗岳旺,周巍,田亮,等. 基于新息χ2检测的扩展抗差卡尔曼滤波及其应用[J]. 武汉大学学报·信息科学版,2016,41(2):269-273)
[16] Wei Wei,Qin Yongyuan,Zhang Xiaodong, et al. Amelioration of the Sage-Husa Algorithm[J]. Journal of Chinese Inertial Technology,2012,20(6):678-686(魏伟,秦永元,张晓东,等. 对Sage-Husa算法的改进[J]. 中国惯性技术学报,2012,20(6):678-686)
[17] Yang Yuanxi. Adaptive Navigation and Kinematic Positioning[M]. Beijing:Surveying and Mapping Press,2006(杨元喜. 自适应动态导航定位[M]. 北京:测绘出版社,2006)
[2] Liu Hui,Darabi H S,Banerjee P,et al. Survey of Wireless Indoor Positioning Techniques and Systems[J]. IEEE Transaction on System,Man,and Cybernetics-Part C:Applications and Reviews, 2007,37(6): 1 067-1 080
[3] Alarifi A,Al-Salman A M,Alsaleh M,et al. Ultra Wideband Indoor Positioning Technologies Analysis and Recent Advances[J]. Sensors,2016,16(5):1-36
[4] De Angelis G ,Moschitta A,Carbone P. Positioning Techniques in Indoor Environments Based on Stochastic Modeling of UWB Round-Trip-Time Measurements[J]. IEEE Transactions on Intelligent Transportation Systems,2016,17(8):2 272-2 281
[5] Schroeder J,Galler S,Kyamakya K,et al. NLOS Detection Algorithms for Ultra-Wideband Localization[C]. Workshop on Positioning,Navigation and Communication, Hannover,Germany,2007
[6] Maran S,Gifford W M,H Wymeersch H,et al. NLOS Identification and Mitigation for Localization Based on UWB Experimental Data[J]. IEEE Journal on Selected Areas in Communications,2011,28(7): 1 026-1 035
[7] Wymeersch H,Maranò S,Gifford W M,et al. A Machine Learning Approach to Ranging Error Mitigation for UWB Localization[J]. IEEE Transactions on Communications,2012,60(6):1 719-1 728
[8] Cui Wei,Wu Chengdong,Zhang Yunzhou,et al. GMM-based Localization Algorithm Under NLOS Conditions[J]. Journal of Communications,2014,35(1):99-106(崔玮,吴成东,张云洲,等. 基于高斯混合模型的非视距定位算法[J]. 通信学,2014,35(1): 99-106)
[9] Li Qiyue,Wu Zhong,Li Jie,et al. NLOS Error Elimination Algorithm Based on Modified Kalman Filtering[J]. Journal of Electronic Measurement and Instrumentation,2015,29(10):1 513-1 519(李奇越,吴忠,黎洁,等. 基于改进卡尔曼滤波的NLOS误差消除算法[J]. 电子测量与仪器学报,2015,29(10):1 513-1 519)
[10] Mao Keji,Wu Jinbin,Jin Hongbo,et al. Indoor Localization Algorithm for NLOS Environment[J]. Acta Electronic Sinica,2016,44(5):1 174-1 179(毛科技,邬锦彬,金洪波,等. 面向非视距环境的室内定位算法[J]. 电子学报,2016,44(5):1 174-1 179)
[11] Yang Y,He H,Xu G. Adaptively Robust Filtering for Kinematic Geodetic Positioning[J]. Journal of Geodesy, 2001,75(2):109-116
[12] Yang Yuanxi,Ren Xia,Xu Yan. Main Progress of Adaptively Robust Filter with Applications in Navigation[J]. Journal of Navigation and Positioning,2013,1(1):9-16(杨元喜,任夏,许艳. 自适应抗差滤波理论及应用的主要进展[J]. 导航定位学报,2013, 1(1):9-15)
[13] Gao Weiguang,Chen Gucang. Integrated GNSS/INS Navigation Algorithms Combining Adaptive Filter with Neural Network[J].Geomatics and Information Science of Wuhan University,2014,39(11):1 323-1 328(高为广,陈谷仓. 结合自适应滤波和神经网络的GNSS/INS抗差组合导航算法[J]. 武汉大学学报·信息科学版,2014,39(11):1 323-1 328)
[14] Tan Xinglong,Wang Jian,Han Houzeng. SVR Aided Adaptive Robust Filtering Algorithm for GPS/INS Integrated Navigation[J].Acta Geodaetica et Cartographica Sinica,2014,43(6):590-597(谭兴龙,王坚,韩厚增. 支持向量回归辅助的GPS/INS组合导航抗差自适应算法[J]. 测绘学报,2014,43(6):590-597)
[15] Miao Yuewang,Zhou Wei,Tian Liang,et al. Extended Robust Kalman Filter Based on Innovation Chi-Square Test Algorithm and Its Application[J]. Geomatics and Information Science of Wuhan University,2016,41(2):269-273(苗岳旺,周巍,田亮,等. 基于新息χ2检测的扩展抗差卡尔曼滤波及其应用[J]. 武汉大学学报·信息科学版,2016,41(2):269-273)
[16] Wei Wei,Qin Yongyuan,Zhang Xiaodong, et al. Amelioration of the Sage-Husa Algorithm[J]. Journal of Chinese Inertial Technology,2012,20(6):678-686(魏伟,秦永元,张晓东,等. 对Sage-Husa算法的改进[J]. 中国惯性技术学报,2012,20(6):678-686)
[17] Yang Yuanxi. Adaptive Navigation and Kinematic Positioning[M]. Beijing:Surveying and Mapping Press,2006(杨元喜. 自适应动态导航定位[M]. 北京:测绘出版社,2006)