Adaptive UKF Method with Applications to Target Tracking
-
摘要: 针对目标跟踪中系统噪声统计特性未知导致滤波发散或者滤波精度不高的问题, 提出了一种自适应无迹卡尔曼滤波(Unscented Kalman filter, UKF)算法.该算法在滤波过程中,利用改进的Sage-Husa估 计器在线估计未知系统噪声的统计特性,并对滤波发散的情况进行判断和抑制, 有效提高了滤波的数值稳定性,减小了状态估计误差. 仿真实验结果表明,与标准UKF算法相比,自适应UKF算法明显改善了目标跟踪的精度和稳定性.Abstract: To improve low filtering precision and divergence caused by unknown system noise statistics in target tracking, an adaptive UKF (Unscented Kalman filter) is proposed. In the filtering process, by introducing the modified Sage-Husa noise statistic estimator, the new algorithm can estimate the statistical parameters of unknown system noises online and restrain the filtering divergence. Therefore, the filter numerical stability is effectively improved and the state estimation error is reduced. Simulation results show that compared with the standard UKF algorithm the proposed algorithm provides better accuracy and stability for target tracking.
-
Key words:
- Target tracking /
- adative filtering /
- unscented Kalman filter (UKF)
-
[1] Bar-Shalom Y, Rong L X, Kirubarajan T. Estimation with Application to Tracking and Navigation: Theory Algorithms and Software. New York: Wiley, 2001. 69-83[2] Sorenson H W. Kalman Filtering: Theory and Application. New York: IEEE, 1985[3] Daum F. Nonlinear filters: beyond the Kalman filter. IEEE Aerospace and Electronic Systems Magazine, 2005, 20(8): 57-69 [4] Athans M, Wisher R P, Bertolini A. Suboptimal state estimation for continuous-time nonlinear systems from discrete noise measurements. IEEE Transactions on Automatic Control, 1968, 13(5): 504-514 [5] Julier S J, Uhlmann J K, Durrant-Whyte H F. A new method for nonlinear transformation of means and covariances in filters and estimators. IEEE Transactions on Automatic Control, 2000, 45(3): 477-482 [6] Julier S J, Uhlmann J K. Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 2004, 92(3): 401-422 [7] Saulson B G, Chang K C. Nonlinear estimation comparison for ballistic missile tracking. Optical Engineering, 2004, 43(6): 1424-1438[8] Xiong K, Chan C, Zhang H S. Detection of satellite attitude sensor faults using the UKF. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(2): 480-491 [9] Sage A, Husa G W. Adaptive filtering with unknown prior statistics. In: Proceedings of Joint Automatic Control Conference. Boulder, USA: American Society of Mechanical Engineers, 1969. 760-769[10] Deng Zi-Li. Self-tuning Fitering Theory with Applications: Modern Time Series Analysis Method. Harbin: Press of Harbin Institute of Technology, 2003. 161-192(邓自立. 自校正滤波理论及其应用: 现代时间序列分析方法. 哈尔滨: 哈尔滨工业大学出版社, 2003. 161-192)[11] Julier S J. The scaled unscented transformation. In: Proceeding of the American Control Conference. Washington D.C., USA: IEEE, 2002. 4555-4559[12] Mehra R K. On the identification of variances and adaptive Kalman filtering. IEEE Transactions on Automatic Control, 1970, 15(2): 175-184[13] Mohamed A H, Schwarz K P. Adaptive Kalman filtering for INS/GPS. Journal of Geodesy, 1999, 73(4): 193-203[14] Mehra R K. Approaches to adaptive filtering. IEEE Transactions on Automatic Control, 1972, 17(5): 693-698[15] Yang Y X, Gao W G. An optimal adaptive Kalman filter. Journal of Geodesy, 2006, 80(4): 177-183[16] Myers K A, Tapley B D. Adaptive sequential estimation with unknown noise statistics. IEEE Transactions on Automatic Control, 1976, 21(4): 520-523[17] Hide C, Moore T, Smith M. Adaptive Kalman filtering for low-cost INS/GPS. Journal of Navigation, 2003, 56(1): 143-152
点击查看大图
计量
- 文章访问数: 2969
- HTML全文浏览量: 67
- PDF下载量: 1692
- 被引次数: 0