Improved Adaptive Filtering Algorithm for GNSS/SINS Integrated Navigation System

LIN Xueyuan; LIU Lili; DONG Yunyun; CHEN Xiangguang; YANG Haili

doi:10.13203/j.whugis20200436

Volume 48 Issue 1

Jan. 2023

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Geomatics and Information Science of Wuhan University > 2023 > 48(1): 127-134. > DOI: 10.13203/j.whugis20200436

LIN Xueyuan, LIU Lili, DONG Yunyun, CHEN Xiangguang, YANG Haili. Improved Adaptive Filtering Algorithm for GNSS/SINS Integrated Navigation System[J]. Geomatics and Information Science of Wuhan University, 2023, 48(1): 127-134. DOI: 10.13203/j.whugis20200436

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Improved Adaptive Filtering Algorithm for GNSS/SINS Integrated Navigation System

1.
Yantai Nanshan University, Yantai 265713, China
2.
Donghai Thermal Power Co. Ltd, Yantai 265713, China

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Received Date: April 08, 2022
Available Online: February 07, 2023
Published Date: January 04, 2023

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Abstract

Abstract

Objectives GNSS/SINS (global navigation satellite system/strapdown inertial navigation system) integrated navigation system has been widely used and researched. In complex environment, GNSS output is prone to four phenomena, including root mean square (RMS) of error sudden change, RMS of error slow change, hard fault and soft fault, which affect the filtering accuracy of integrated navigation system and the navigation safety of carrier.
Methods In order to solve the above problems, an improved adaptive filtering algorithm for GNSS/SINS integrated navigation system is proposed. Firstly, an observation factor was constructed by using the observation anomaly test statistics and filter threshold value in the filtering process. Then, the variable Bayesian principle and the outlier-restrained filtering method are combined to design an improved adaptive filtering algorithm for the integrated navigation system.
Results The simulation experiment shows that compared with the conventional algorithms, the proposed algorithm can improve the position accuracy and speed accuracy by about 11.8% and 13.7%, respectively, when the RMS of GNSS output error changes. And it can also improve the position accuracy and speed accuracy by about 70.8% and 69.6%, respectively, when a hard fault occurs on the GNSS output.
Conclusions The experimental results show that the proposed algorithm has strong adaptability and can improve the accuracy and continuous availability of integrated navigation systems in complex environments.
- variational Bayesian,
- adaptive filtering,
- outlier-restrained filtering,
- integrated navigation system,
- observation factor

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[1]	赵昂, 杨元喜, 许扬胤, 等. GNSS单系统及多系统组合完好性分析[J]. 武汉大学学报(信息科学版), 2020, 45(1): 72-80. doi: 10.13203/j.whugis20180425 Zhao Ang, Yang Yuanxi, Xu Yangyin, et al. Integrity Analysis of GNSS Single System and Multi-System Combination[J]. Geomatics and Information Science of Wuhan University, 2020, 45(1): 72-80. doi: 10.13203/j.whugis20180425
[2]	李博琳, 徐彬, 王凤聚, 等. 基于变分贝叶斯原理的SINS/GPS组合导航VB-CKF滤波算法[J]. 科学技术与工程, 2019, 19(28): 396-400. https://www.cnki.com.cn/Article/CJFDTOTAL-KXJS201928062.htm Li Bolin, Xu Bin, Wang Fengju, et al. VB-CKF for SINS/GPS Integrated Navigation System Based on Variational Bayesian Theory[J]. Science Technology and Engineering, 2019, 19(28): 396-400. https://www.cnki.com.cn/Article/CJFDTOTAL-KXJS201928062.htm
[3]	王鼎杰, 孟德利, 李朝阳, 等. 抗野值自适应卫星/微惯性组合导航方法[J]. 仪器仪表学报, 2017, 38(12): 2952-2958. https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201712009.htm Wang Dingjie, Meng Deli, Li Zhaoyang, et al. Adaptively Outlier-Restrained GNSS/MEMS-INS Integrated Navigation Method[J]. Chinese Journal of Scientific Instrument, 2017, 38(12): 2952-2958. https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201712009.htm
[4]	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. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH202206021.htm 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. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH202206021.htm
[5]	Soken H E, Hajiyev C. Adaptive Unscented Kalman Filter with Multiple Fading Factors for Pico Satellite Attitude Estimation[C]//The 4th International Conference on Recent Advances in Space Technologies, Istanbul, Turkey, 2009.
[6]	熊智, 邵慧, 华冰, 等. 改进故障隔离的容错联邦滤波[J]. 航空学报, 2015, 36(3): 929-938. https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB201503027.htm Xiong Zhi, Shao Hui, Hua Bing, et al. An Improved Fault Tolerant Federated Filter with Fault Isolation[J]. Acta Aeronautica et Astronautica Sinica, 2015, 36(3): 929-938. https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB201503027.htm
[7]	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. https://www.cnki.com.cn/Article/CJFDTOTAL-CHWZ201301005.htm 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): 9-15. https://www.cnki.com.cn/Article/CJFDTOTAL-CHWZ201301005.htm
[8]	徐定杰, 沈忱, 沈锋. 时变有色观测噪声下基于变分贝叶斯学习的自适应卡尔曼滤波[J]. 电子与信息学报, 2013, 35(7): 1593-1598. https://www.cnki.com.cn/Article/CJFDTOTAL-DZYX201307012.htm Xu Dingjie, Shen Chen, Shen Feng. Adaptive Kalman Filtering with Time-Varying Colored Measurement Noise by Variational Bayesian Learning[J]. Journal of Electronics & Information Technology, 2013, 35(7): 1593-1598. https://www.cnki.com.cn/Article/CJFDTOTAL-DZYX201307012.htm
[9]	Lim K L, Wang H. MAP Approximation to the Variational Bayes Gaussian Mixture Model and Application[J]. Soft Computing, 2018, 22(10): 3287-3299.
[10]	Vrettas M D, Cornford D, Opper M. Estimating Parameters in Stochastic Systems: A Variational Bayesian Approach[J]. Physica D: Nonlinear Phenomena, 2011, 240(23): 1877-1900.
[11]	林雪原, 李荣冰, 高青伟. 组合导航及其信息融合方法[M]. 北京: 国防工业出版社, 2017. Lin Xueyuan, Li Rongbing, Gao Qingwei. Integrated Navigation and Its Information Fusion Method[M]. Beijing: National Defense Industry Press, 2017.
[12]	林雪原, 孙玉梅, 董云云, 等. 多传感器组合导航系统的改进多尺度滤波算法[J]. 中国空间科学技术, 2020, 40(4): 61-68. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGKJ202004009.htm Lin Xueyuan, Sun Yumei, Dong Yunyun, et al. Improved Multi-Scale Filtering Algorithm of Multi-Sensor Integrated Navigation System[J]. Chinese Space Science and Technology, 2020, 40(4): 61-68. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGKJ202004009.htm
[13]	戴卿, 隋立芬, 田源, 等. 变分优化的高斯混合滤波及其在导航中的应用[J]. 武汉大学学报(信息科学版), 2019, 44(5): 699-705. doi: 10.13203/j.whugis20170235 Dai Qing, Sui Lifen, Tian Yuan, et al. Gaussian Mixture Filter Based on Variational Bayesian Learning Optimization and Its Application to Integrated Navigation[J]. Geomatics and Information Science of Wuhan University, 2019, 44(5): 699-705. doi: 10.13203/j.whugis20170235
[14]	Dai Q, Sui L F, Wang L X, et al. An Efficiency Algorithm on Gaussian Mixture UKF for BDS/INS Navigation System[J]. Geodesy and Geodynamics, 2018, 9(2): 169-174.
[15]	魏二虎, 孙聃石, 万伟, 等. 一种针对大失准角捷联惯导系统初始对准的处理方法[J]. 测绘地理信息, 2020, 45(6): 7-11. https://www.cnki.com.cn/Article/CJFDTOTAL-CHXG202006002.htm Wei Erhu, Sun Danshi, Wan Wei, et al. A Processing Method for Strapdown Inertial Navigation System Initial Alignment Under Large Misalignment Angles[J]. Journal of Geomatics, 2020, 45(6): 7-11. https://www.cnki.com.cn/Article/CJFDTOTAL-CHXG202006002.htm
[16]	潘泉, 胡玉梅, 兰华, 等. 信息融合理论研究进展: 基于变分贝叶斯的联合优化[J]. 自动化学报, 2019, 45(7): 1207-1223. https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201907001.htm Pan Quan, Hu Yumei, Lan Hua, et al. Information Fusion Progress: Joint Optimization Based on Variational Bayesian Theory[J]. Acta Automatica Sinica, 2019, 45(7): 1207-1223. https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201907001.htm
[17]	Sarkka S, Nummenmaa A. Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations[J]. IEEE Transactions on Automatic Control, 2009, 54(3): 596-600.
[18]	宋会杰, 董绍武, 王燕平, 等. 基于渐消因子的改进Kalman滤波时间尺度估计算法[J]. 武汉大学学报(信息科学版), 2019, 44(8): 1205-1211. doi: 10.13203/j.whugis20180065 Song Huijie, Dong Shaowu, Wang Yanping, et al. An Improved Kalman Filter Time Scale Algorithm Based on Forgetting Factor[J]. Geomatics and Information Science of Wuhan University, 2019, 44(8): 1205-1211. doi: 10.13203/j.whugis20180065
[19]	唐炉亮, 杨雪, 靳晨, 等. 基于约束高斯混合模型的车道信息获取[J]. 武汉大学学报(信息科学版), 2017, 42(3): 341-347. doi: 10.13203/j.whugis20140965 Tang Luliang, Yang Xue, Jin Chen, et al. Traffic Lane Number Extraction Based on the Constrained Gaussian Mixture Model[J]. Geomatics and Information Science of Wuhan University, 2017, 42(3): 341-347. doi: 10.13203/j.whugis20140965
[20]	Stepanov O A, Chelpanov I B, Motorin A V. Accuracy of Sensor Bias Estimation and Its Relationship with Allan Variance[J]. Gyroscopy and Navigation, 2017, 8(1): 51-57.
[21]	张浩, 肖勇, 杨朝旭, 等. 基于双状态卡方故障检测的组合导航系统[J]. 航空学报, 2020, 41(S2): 724271. https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB2020S2006.htm Zhang Hao, Xiao Yong, Yang Chaoxu, et al. Integrated Navigation System Based on Fault Detection Using Double State Chi-Square Test[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(S2): 724271. https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB2020S2006.htm

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Improved Adaptive Filtering Algorithm for GNSS/SINS Integrated Navigation System

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