基于加權(quán)自適應(yīng)平方根容積卡爾曼濾波的GPS/INS組合導(dǎo)航方法
doi: 10.11999/JEIT170597
基金項(xiàng)目:
國家自然科學(xué)基金(61301094, 61473308, 61501430)
A GPS/INS Integrated Navigation Method Based on Weighting Adaptive Square-root Cubature Kalman Filter
Funds:
The National Natural Science Foundation of China (61301094, 61473308, 61501430)
-
摘要: 針對GPS/INS組合導(dǎo)航系統(tǒng)中，由于量測噪聲統(tǒng)計(jì)的不確定性導(dǎo)致平方根容積卡爾曼濾波器(SCKF)濾波精度下降甚至發(fā)散的問題，該文提出一種基于加權(quán)的自適應(yīng)SCKF(WASCKF)方法。該方法首先利用移動(dòng)開窗理論對SCKF新息的協(xié)方差陣進(jìn)行最大似然估計(jì)，實(shí)現(xiàn)對測量噪聲統(tǒng)計(jì)特性的在線調(diào)整；然后，利用加權(quán)理論，依據(jù)窗口內(nèi)不同時(shí)刻信息的有用程度的不同而設(shè)置相應(yīng)的權(quán)值，增強(qiáng)對窗口內(nèi)有用信息的利用。最后，將WASCKF方法應(yīng)用于GPS/INS組合導(dǎo)航系統(tǒng)中進(jìn)行仿真驗(yàn)證，并與SCKF和ASCKF方法進(jìn)行比較，結(jié)果表明，在測量噪聲統(tǒng)計(jì)存在不確定情況下，該文所提出方法的速度誤差和位置誤差的均方根均小于SCKF和ASCKF方法，能夠有效地提高GPS/INS組合導(dǎo)航系統(tǒng)對量測噪聲統(tǒng)計(jì)不確定的自適應(yīng)能力與導(dǎo)航性能。Abstract: In the GPS/INS integrated navigation system, the filtering precision of Square-root Cubature Kalman Filter (SCKF) will decrease or even diverge resulting from the uncertainty of the measured noise statistics, therefore, a Weighting Aaptive Square-root Cubature Kalman Filter (WASCKF) method is proposed in this paper. Firstly, the moving window method is employed to conduct the maximum likelihood estimation of the covariance matrix of SCKF, in order to realize the on-line adjustment of the statistical characteristics of the measured noise. Then, the weighting theory is utilized to set the corresponding weights according to the usefulness of the information at different times in the window, thus it takes great use of effective information in the window. Finally, the WASCKF is applied to the GPS/INS integrated navigation system for simulation and verification, and comparing with the SCKF and ASCKF methods. The results indicate that the mean square root of velocity errors and position errors of the proposed method are less than SCKF and ASCKF, and it can effectively improve the adaptive capability and navigation performance of GPS/INS integrated navigation system with the measured noise uncertainty.
-
Key words:
- Integrated navigation /
- Measured noise /
- Cubature Kalman Filter (CKF) /
- Weighting
-
TSENG Chienhao, LIN Shengfuu, and JWO Dahjing. Fuzzy adaptive cubature Kalman filter for integrated navigation systems[J]. Sensors, 2016, 16(8): 1167-1189. doi: 10.3390/ s16081167. LI Zengke, WANG Jingxiang, WANG Jian, et al. PPP/INS tightly coupled navigation using adaptive federated filter[J]. GPS Solutions, 2017, 21(1): 1-12. doi: 10.1007/s10291-015- 0511-z. WANG Liansheng and XIA Yuanqing. Mars Entry Navigation with uncertain parameters based on desensitized extended Kalman filter[J]. IEEE Transactions on Industrial Informatics, 2015, 11(5): 998-1005. doi: 10.1109/TII.2015. 2463763. LI Donggong, JI Daxiong, LIU Jian, et al. A Multi-model EKF integrated navigation algorithm for deep water AUV[J]. International Journal of Advanced Robotic Systems, 2016, 13(1): 1-15. doi: 10.5772/62076. YAZDKHASTI S, SASIADEK J, and ULRICH S. Performance enhancement for GPS/INS fusion by using a fuzzy adaptive unscented Kalman filter[C]. 2016 21st International Conference on Methods and Models in Automation and Robotics, Poland, 2016: 1194-1199. doi: 10.1109/MMAR.2016.7575308. LI Kailong, CHANG Lubin, and HU Baiqing. A variational bayesian-based unscented Kalman filter with both adaptivity and robustness[J]. IEEE Sensors Journal, 2016, 16(18): 6966-6976. doi: 10.1109/JSEN.2016.2591260. ARASARATNAM I and HAYKIN S. Cubature Kalman filters[J]. IEEE Transactions on Automatic Control, 2009, 54(6): 510-518. doi: 10.1109/TAC.2009.2019800. XU Bo, ZHANG Peng, WEN Hongzhi, et al. Stochastic stability and performance analysis of Cubature Kalman Filter[J]. Neurocomputing, 2015, 186: 218-227. doi: 10.1016 /j.neucom.2015.12.087. CHANDRA K, GU D, and POSTLETHWAITE I. Square root cubature information filter[J]. IEEE Sensors Journal, 2013, 13(2): 750-758. doi: 10.1109/JSEN.2012.2226441. WANG Dingjie, L Hanfeng, and WU Jie. Augmented cubature Kalman filter for nonlinear RTK/MIMU integrated navigation with non-additive noise[J]. Measurement Journal of the International Measurement Confederation, 2017, 97: 111-125. doi: 10.1016/j.measurement.2016.10.056. CUI Bingbo, CHEN Xiyuan, and TANG Xinhua. Improved cubature Kalman filter for GNSS/INS based on transformation of posterior sigma-points error[J]. IEEE Transactions on Signal Processing, 2017, 65(11): 2975-2987. doi: 10.1109/TSP.2017.267968. JIANG Changhui, CHEN Shuai, BO Yuming, et al. Performance improvement of GPS/SINS ultra-tightly integrated navigation system utilizing a robust Cubature Kalman Filter[J]. Journal of Aeronautics, Astronautics and Aviation, 2017, 49(1): 49-55. doi: 10.6125/16-1201-916. SHI Yuepeng, CHE Lei, GE Quanbo, et al. Adaptive high-degree Cubature Kalman Filter with unknown noise statistics[J]. Journal of Information and Computational Science, 2014, 11(18): 6703-6712. doi: 10.12773/jics20105083. JIANG Chen, ZHANG Shubi, and ZHANG Qiuzhao. A novel robust interval Kalman filter algorithm for GPS/INS integrated navigation[J]. Journal of Sensors, 2016, 2016: ID 3727241. doi: 10.1155/2016/3727241. YUE Zhe, LIAN Baowang, and TANG Chengkai. The GPS/INS integrated navigation method based on adaptive SSR-SCKF cubature Kalman filter[C]. The 8th China Satellite Navigation Conference, Shanghai, 2017: 395-405. doi: 10.1007/978-981-10-4591-2_32. 胡宇, 張世英, 羅雷. 基于自適應(yīng)容積卡爾曼濾波方法的渦扇發(fā)動(dòng)機(jī)氣路部件故障診斷[J]. 航空動(dòng)力學(xué)報(bào), 2016, 31(5): 1260-1267. doi: 10.13224/j.cnki.jasp.2016.05.030. HU Yu, ZHANG Shiying, and LUO Lei. Turbofan engine gas path components fault diagnosis based on adaptive cubature Kalman filter[J]. Journal of Aerospace Power, 2016, 31(5): YANG Yuanxi and ZHANG Shuangcheng. Adaptive fitting of systematic errors in navigation[J]. Journal of Geodesy, 2005, 79(1): 43-49. doi: 10.1007/s00190-005-0441-6. GAO Shesheng, HU Gaoge, and ZHONG Yongmin. Window and random weighting-based adaptive unscented Kalman filter[J]. International Journal of Adaptive Control and Signal Processing, 2015, 29: 201-223. doi: 10.1002/acs.2467. -1267. doi: 10.13224/j.cnki.jasp.2016.05.030. -
計(jì)量
- 文章訪問數(shù): 1599
- HTML全文瀏覽量: 187
- PDF下載量: 318
- 被引次數(shù): 0
下載:
