采用改进粒子滤波的雷达扩展目标检测前跟踪

doi:10.3969/j.issn.1001-2400.2011.02.018

J4 ›› 2011, Vol. 38 ›› Issue (2): 99-104.doi: 10.3969/j.issn.1001-2400.2011.02.018

采用改进粒子滤波的雷达扩展目标检测前跟踪

吴兆平;朱凯然;苏涛;卢锦

(西安电子科技大学雷达信号处理国家重点实验室，陕西西安 710071)

收稿日期:2010-03-10 出版日期:2011-04-20 发布日期:2011-05-26
通讯作者: 吴兆平
作者简介:吴兆平(1984-)，男，西安电子科技大学博士研究生，E-mail: zpwu@mail.xidian.edu.cn．
基金资助:
国家自然科学基金资助项目(60901065)

Extended radar target tracking before detection using the modified particle filter

WU Zhaoping;ZHU Kairan;SU Tao;LU Jin

(National Lab. of Radar Signal Processing, Xidian Univ., Xi'an 710071， China)

Received:2010-03-10 Online:2011-04-20 Published:2011-05-26
Contact: WU Zhaoping

摘要/Abstract

摘要：

在雷达回波的距离——多普勒数据上，建立了粒子滤波所需的系统动态模型和观测模型，将传统的点目标模型改进为更接近于实际的线形扩展目标模型，并推导出了新模型的似然比函数．同时，为了减少粒子数，对传统的粒子滤波(PF)算法进行了改进．由于模型的匹配和检测前跟踪(TBD)算法在时间上的积累功能，所提算法提高了雷达对低信噪比目标的检测概率．仿真结果表明，该算法能稳定地检测到信噪比为1dB的目标．

关键词: 检测前跟踪, 粒子滤波, 扩展目标, 似然比函数

Abstract:

The system dynamic model and measurement model in the particle filter(PF) are established in a sequence of radar range-Doppler measurements and a linear extended target model is proposed, which is more suitable for describing a maneuvering target than the conventional point target model. Furthermore, the likelihood ratio function of the new model is also derived. In addition, the conventional PF is improved here to reduce the number of particles. Due to the macthing between the proposed target model and the integration function of TBD algorithm over time, an improved probability of detection for the dim target is obtained. Simulation results demonstrate that the proposed method is capable of detecting and tracking a target with the SNR of 1dB stably.

Key words: track-before-detect, particle filter, extended target, likelihood ratio function

吴兆平;朱凯然;苏涛;卢锦. 采用改进粒子滤波的雷达扩展目标检测前跟踪[J]. J4, 2011, 38(2): 99-104.

WU Zhaoping;ZHU Kairan;SU Tao;LU Jin. Extended radar target tracking before detection using the modified particle filter[J]. J4, 2011, 38(2): 99-104.

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采用改进粒子滤波的雷达扩展目标检测前跟踪

Extended radar target tracking before detection using the modified particle filter

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