基于JMS-SMC-PHD滤波的检测前跟踪算法

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。

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

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.