Nonlinear system,Maneuver target tracking,Correlated noises,Square-root cubature Kalman filter (SCKF),Strong tracking filtering (STF)," /> SCKF-STF-CN: a universal nonlinear filter for maneuver target tracking" /> SCKF-STF-CN: a universal nonlinear filter for maneuver target tracking" /> Nonlinear system,Maneuver target tracking,Correlated noises,Square-root cubature Kalman filter (SCKF),Strong tracking filtering (STF),"/> <span style="font-size:13.3333px;">SCKF-STF-CN: a universal nonlinear filter for maneuver target tracking</span>
Please wait a minute...
FITEE
Front. Inform. Technol. Electron. Eng.  2011, Vol. 12 Issue (8): 678-686    DOI: 10.1631/jzus.C10a0353
    
SCKF-STF-CN: a universal nonlinear filter for maneuver target tracking
Quan-bo Ge1,2, Wen-bin Li1, Cheng-lin Wen*,1
1 Institute of Information and Control, Hangzhou Dianzi University, Hangzhou 310018, China 2 State Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
SCKF-STF-CN: a universal nonlinear filter for maneuver target tracking
Quan-bo Ge1,2, Wen-bin Li1, Cheng-lin Wen*,1
1 Institute of Information and Control, Hangzhou Dianzi University, Hangzhou 310018, China 2 State Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
 全文: PDF(313 KB)  
摘要： Square-root cubature Kalman filter (SCKF) is more effective for nonlinear state estimation than an unscented Kalman filter. In this paper, we study the design of nonlinear filters based on SCKF for the system with one step noise correlation and abrupt state change. First, we give the SCKF that deals with the one step correlation between process and measurement noises, SCKF-CN in short. Second, we introduce the idea of a strong tracking filter to construct the adaptive square-root factor of the prediction error covariance with a fading factor, which makes SCKF-CN obtain outstanding tracking performance to the system with target maneuver or abrupt state change. Accordingly, the tracking performance of SCKF is greatly improved. A universal nonlinear estimator is proposed, which can not only deal with the conventional nonlinear filter problem with high dimensionality and correlated noises, but also achieve an excellent strong tracking performance towards the abrupt change of target state. Three simulation examples with a bearings-only tracking system are illustrated to verify the efficiency of the proposed algorithms.
关键词： Nonlinear system')" href="#">Nonlinear system;  Maneuver target tracking;  Correlated noises;  Square-root cubature Kalman filter (SCKF);  Strong tracking filtering (STF)    
Abstract: Square-root cubature Kalman filter (SCKF) is more effective for nonlinear state estimation than an unscented Kalman filter. In this paper, we study the design of nonlinear filters based on SCKF for the system with one step noise correlation and abrupt state change. First, we give the SCKF that deals with the one step correlation between process and measurement noises, SCKF-CN in short. Second, we introduce the idea of a strong tracking filter to construct the adaptive square-root factor of the prediction error covariance with a fading factor, which makes SCKF-CN obtain outstanding tracking performance to the system with target maneuver or abrupt state change. Accordingly, the tracking performance of SCKF is greatly improved. A universal nonlinear estimator is proposed, which can not only deal with the conventional nonlinear filter problem with high dimensionality and correlated noises, but also achieve an excellent strong tracking performance towards the abrupt change of target state. Three simulation examples with a bearings-only tracking system are illustrated to verify the efficiency of the proposed algorithms.
Key words: Nonlinear system    Maneuver target tracking    Correlated noises    Square-root cubature Kalman filter (SCKF)    Strong tracking filtering (STF)
收稿日期: 2010-07-29 出版日期: 2011-08-03
CLC:  TP2  
服务　　
把本文推荐给朋友 SCKF-STF-CN: a universal nonlinear filter for maneuver target tracking”的文章，特向您推荐。请打开下面的网址：http://www.zjujournals.com/xueshu/fitee/CN/abstract/abstract15065.shtml" name="neirong"> SCKF-STF-CN: a universal nonlinear filter for maneuver target tracking">
加入引用管理器
E-mail Alert
RSS
作者相关文章　　
Quan-bo Ge
Wen-bin Li
Cheng-lin Wen

引用本文:

Quan-bo Ge, Wen-bin Li, Cheng-lin Wen. SCKF-STF-CN: a universal nonlinear filter for maneuver target tracking. Front. Inform. Technol. Electron. Eng., 2011, 12(8): 678-686.

链接本文:

http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C10a0353    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2011/V12/I8/678

[1] Yu-shi Zhu, Can-jun Yang, Shi-jun Wu, Qing Li, Xiao-le Xu. 适用于湖水监测的水下滑翔机的空间高效转向方法[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(4): 485-497.
[2] Guo-liang Tao, Ce Shang, De-yuan Meng, Chao-chao Zhou. 含参数初值整定和自适应鲁棒方法的3-RPS气动并联平台位姿控制[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(3): 303-316.
[3] Guo-liang Tao, Ce Shang, De-yuan Meng, Chao-chao Zhou. 含参数初值整定和自适应鲁棒方法的3-RPS气动并联平台位姿控制[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(3): 303-316.
[4] Tao Zhang, Qing Li, Chang-shui Zhang, Hua-wei Liang, Ping Li, Tian-miao Wang, Shuo Li, Yun-long Zhu, Cheng Wu. 智能无人自主系统发展趋势[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 68-85.
[5] Xiao-yu ZHANG. 一类非仿射离散非线性系统的直接自适应模糊滑模控制[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(12): 1331-1343.
[6] Yong-chun Xie, Huang Huang, Yong Hu, Guo-qi Zhang. 先进控制方法在航天器上的应用：进展、挑战和未来发展[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(9): 841-861.
[7] Guo-jiang Shen, Yong-yao Yang. 一种城市主干道信号动态协调控制方法及其应用[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(9): 907-918.
[8] Wei Yang, Can-jun Yang, Ting Xu. 基于人体髋关节转动中心分析的髋关节外骨骼仿生设计[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(8): 792-802.
[9] Xiao-xin Fu, Yong-heng Jiang, De-xian Huang, Jing-chun Wang, Kai-sheng Huang. 基于候选曲线的公路轨迹规划中的智能计算量分配[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(6): 553-565.
[10] Kyong-il Kim, Hsin Guan, Bo Wang, Rui Guo, Fan Liang. 铰接车辆的主动转向控制策略研究[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(6): 576-586.
[11] Qiang Liu, Jia-chen Ma. 基于子空间的离散时滞系统辨识[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(6): 566-575.
[12] Xie Wang, Mei-qin Liu, Zhen Fan, Sen-lin Zhang. 目标跟踪中一种新的基于H∞滤波器的噪声统计特征估计方法[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(5): 449-457.
[13] Yang Zhou, De-wei Wu. 基于头朝向细胞和网格细胞的生物启发式路径整合模型[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(5): 435-448.
[14] Jin-yi Liu, Jing-quan Tan, En-rong Mao, Zheng-he Song, Zhong-xiang Zhu. 基于比例控制的农业机械自动转向系统研究[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(5): 458-464.
[15] Feng-yu Zhou, Xian-feng Yuan, Yang Yang, Zhi-fei Jiang, Chen-lei Zhou. 一种室内移动机器人高精度视觉定位传感器及其工作原理[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(4): 365-374.