基于JMS-SMC-PHD滤波的检测前跟踪算法  

A Track-Before-Detect Algorithm Based on a JMS-SMC-PHD Filter

作　　者：薛秋条[1] 宁巧娇 吴孙勇[1,2] 蔡如华 伍雯雯[1] XUE Qiutiao;NING Qiaojiao;WU Sunyong;CAI Ruhua;WU Wenwen(School of Mathematics and Computing Science,Guilin University of Electronic Technology,Guilin 541004,China;Guangxi Key Laboratory of Cryptography and Information Security,Guilin 541004,China)

机构地区：[1]桂林电子科技大学数学与计算科学学院,广西桂林541004 [2]广西密码学与信息安全重点实验室,广西桂林541004

出　　处：《红外技术》2020年第8期783-788,共6页Infrared Technology

基　　金：国家自然科学基金项目(61861008,11661024);广西研究生教育创新计划项目(2020YCXS084);广西高校数据分析与计算重点实验室开放基金项目资助。

摘　　要：针对低信噪比条件下机动目标的检测与跟踪问题,提出跳跃马尔可夫系统下的序贯蒙特卡罗概率假设密度(JMS-SMC-PHD)滤波的检测前跟踪算法。该算法在机动目标数目和模型未知情况下,直接利用红外传感器量测数据,通过在目标状态矢量中增加模型变量并利用马尔可夫模型概率转移矩阵结合序贯蒙特卡罗概率假设密度(SMC-PHD)滤波,实现机动弱小目标的检测前跟踪。仿真结果表明所提方法可以有效地实现目标的检测与跟踪。In view of the problem of detecting and tracking maneuvering small targets at low signal-to-noise,a track-before-detect algorithm based on sequential Monte Carlo probability hypothesis density filtering for Jump-Markov systems(JMS-SMC-PHD)is presented.Under the condition of an unknown number of maneuvering targets and unknown models,the algorithm achieves track-before-detect of small maneuvering targets by using measurement data from infrared sensors directly,adding a variable that denotes the dynamics model of the target,and using a Markov model probability transfer matrix combined with an SMC-PHD filter.Simulation results show that the proposed method can effectively implement target detection and tracking performance.

关 键 词：检测前跟踪 跳跃马尔可夫系统 概率假设密度滤波 序贯蒙特卡罗 机动弱小目标 

分 类 号：TN953[电子电信—信号与信息处理]