Strong tracking filtering (STF) sets small threshold to judge filtering divergence leading to fading factor with high probability, which causes excessive regulation of the filtering gain and makes the state estimation curve lack smoothness. By analyzing the operation mechanism of STF, improved STF (ISTF) is proposed. The proposed algorithm reduces probability of misjudging filter divergence by appropriately increasing the threshold. It determines the softening factor to suit different dimensions of the measurement equation, and thus avoids the disadvantages of the previous methods that determine the softening factor according to experiences. Simulation indicates that ISTF can maintain filtering accuracy and estimation smoothness, and is robust against sudden changes in the system state, showing its feasibility and effectiveness.
QIAN Hua-ming1, GE Lei1,2, HUANG Wei1
. An Improved Strong Tracking Filtering Algorithm[J]. Journal of Applied Sciences, 2015
, 33(1)
: 32
-40
.
DOI: 10.3969/j.issn.0255-8297.2015.01.004
[1] HUSOY J H, ABADI M S E. Unified approach to adaptive filters and their performance [J]. IET Signal Processing, 2008, 2(2): 97-109.
[2] SORENSON H W, SACKS J E. recursive fading memory filtering[J] Information Sciences, 1971, 3(2): 101-119.
[3] YDSTID B E, Co T. recursive estimation with adaptive divergence control[J]. IEEE Proceeding Control Theory and Applications, 1985, 132(3): 124-130.
[4] 周东华，席裕庚，张钟俊. 一种带多重次优渐消因子的扩展卡尔曼滤波器[J]. 自动化学报，1991, 17(6): 689-695.
ZHOU Donghua, XI Yugeng, ZHANG Zhongjun. A suboptimal multiple fading extended Kalman filter[J]. Acta Automation Sinica, 1991, 17(6): 689-695. (in Chinese)
[5] 鲍其莲，孙朔冬，刘英. 动基座传递对准非线性滤波器的设计及应用[J]. 中国惯性技术学报，2010, 18(1): 33-37.
BAO Qi Lian, SUN Shuo Dong, LIU Ying. Design and application of nonlinear Kalman filter in moving base alignment of inertial navigation systems[J]. Journal of Chinese Inertial Technology, 2010, 18(1): 33-37. (in Chinese)
[6] 王虎，王解先，白贵霞，李浩军. 改进的渐消卡尔曼滤波在GPS动态定位中的应用 [J]. 同济大学学报：自然科学版，2011, 39(1): 124-128.
WANG Hu, WANG Jie Xian, BAI Gui Xia, LI Hao Jun. An improved fading Kalman filter and its application to GPS Kinematic positioning [J]. Journal of Tongji University : Natural Science , 2011, 39(1): 124-128. (in Chinese)
[7] XU T L, GE Q B, FENG X L, WEN C L. Strong tracking filter with bandwidth constraint for sensor networks [C]// IEEE International Conference on Control and Automation, Xiamen, China: 2010, 596-601.
[8] LIU Z B, SHI Z Y, XU W L. Multi-component information-equalized extended strong tracking filter for global localization: a scheme robust to kidnapping and symmetrical environments[J]. Robotics and Autonmous Systems, 2010, 58(5): 465-487.
[9] JWO D J, WANG S H. Adaptive fuzzy strong tracking extended Kalman filtering for GPS navigation [J]. IEEE Sensors Journal, 2007, 7(5): 778-789.
[10] ZHAO S E, LI Y L. Multi-sensor information fusion and strong tracking filter for vehicle nonlinear state estimation [C]// IEEE Intelligent Vehicles Symposium, Xi’an, 2009: 747-751
[11] XU W M, KUANG L L, LU J H. Positioning algorithm with joint space–time constraints for unmanned network-flying vehicles [J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(8): 2694-2704.
[12] XING J L, AI H Z, LIU L W, LAO S H. Multiple player tracking in sports video: a dual-mode two-way bayesian inference approach with progressive observation modeling [J]. IEEE Transactions on Image Processing, 2011, 20(6): 1652-1667.
[13] YESTE OJEDA O A, GRAJAL J. Adaptive-FRESH filters for compensation of cycle-frequency errors [J]. IEEE Transactions on Signal Processing, 2011, 52(1): 1-10.
[14] BENESTY J, PALEOLOGU C, CIOCHINA S. On regularization in adaptive filtering [J] IEEE Transactions on Audio, Speech, and Language Processing, 2011, 19(6): 1734-1742.
[15] 邓自立，王建国. 非线性系统的自适应推广的Kalman滤波[J]. 自动化学报，1987, 13(5): 375-379.
DENG Zili, WANG Jianguo. Adaptive extended Kalman filtering for nonlinear systems [J]. Acta Automatica Sinica, 1987, 13(5): 375-379. (in Chinese)
