Tracking with nonlinear measurement model by coordinate rotation transformation

被引：0

作者：

ZENG Tao ^{[1
]}

LI Chun Xia ^{[1
]}

LIU Quan Hua ^{[1
]}

CHEN Xin Liang ^{[1
]}

机构：

[1] School of Information and Electronics,Beijing Institute of Technology

来源：

Science China(Technological Sciences) | 2014年 / 12期

基金：

中国国家自然科学基金;

关键词：

target tracking; Kalman filtering; nonlinear filtering; decoupled; nonlinearity;

D O I：

暂无

中图分类号：

TN713 [滤波技术、滤波器];

学科分类号：

080902 ;

摘要：

A new filtering method is proposed to accurately estimate target state via decreasing the nonlinearity between radar polar measurements(or spherical measurements in three-dimensional(3D) radar) and target position in Cartesian coordinate. The degree of linearity is quantified here by utilizing correlation coefficient and Taylor series expansion. With the proposed method, the original measurements are converted from polar or spherical coordinate to a carefully chosen Cartesian coordinate system that is obtained by coordinate rotation transformation to maximize the linearity degree of the conversion function from polar/spherical to Cartesian coordinate. Then the target state is filtered along each axis of the chosen Cartesian coordinate. This method is compared with extended Kalman filter(EKF), Converted Measurement Kalman filter(CMKF), unscented Kalman filter(UKF) as well as Decoupled Converted Measurement Kalman filter(DECMKF). This new method provides highly accurate position and velocity with consistent estimation.

引用

页码：2396 / 2406

页数：11

共 16 条

[11] Particle filter with multimode sampling strategy
Zuo, Junyi
Liang, Yan
Zhang, Yizhe
Pan, Quan
[J]. SIGNAL PROCESSING, 2013, 93 (11) : 3192 - 3201
[12] Joint tracking and discrimination of exoatmospheric active decoys using nine-dimensional parameter-augmented EKF
Rao, Bin
Xiao, Shunping
Wang, Xuesong
[J]. SIGNAL PROCESSING, 2011, 91 (10) : 2247 - 2258
[13] Globally optimal distributed Kalman filtering fusion[J]. SHEN XiaoJing, LUO YingTing , ZHU YunMin & SONG EnBin College of Mathematics, Sichuan University, Chengdu 610064, China.Science China(Information Sciences). 2012(03)
[14] Unscented extended Kalman filter for target tracking[J]. Changyun Liu1,2, Penglang Shui1, and Song Li2 1. National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, P. R. China;2. Missile College of Air Force Engineering University, Sanyuan 713800, P. R. China.Journal of Systems Engineering and Electronics. 2011(02)
[15] A novel strong tracking finite-difference extended Kalman filter for nonlinear eye tracking[J]. ZHANG ZuTao1,2? & ZHANG JiaShu1 1 School of Mechanical Engineering,Southwest Jiaotong University,Chengdu 610031,China;2 Sichuan Key Lab of Signal and Information Processing,Southwest Jiaotong University,Chengdu 610031,China.Science in China(Series F:Information Sciences). 2009(04)
[16] 解耦的转换测量Kalman滤波算法
王宏强
黎湘
庄钊文
郭桂蓉
[J]. 电子学报, 2003, (06) : 867 - 870

← 1 2 →