基于辅助粒子滤波的机动弱目标TBD算法

A TBD Algorithm for Maneuvering Stealthy Target Based on Auxiliary Particle Filtering

为了解决低信噪比条件下的机动目标检测跟踪问题,研究了辅助粒子滤波与多模粒子滤波(MMPF)相结合的检测前跟踪(APF-MMPF)算法。将多模粒子滤波过程中包含目标存在变量及运动模式变量的预测粒子直接用于产生辅助变量,进行辅助粒子滤波过程实现对机动目标的检测跟踪。通过APF-MMPF算法与单纯MMPF算法的仿真结果对比可见,APF-MMPF算法的检测概率高、跟踪误差小,检测跟踪性能优于MMPF算法。由算法机理和仿真结果可见,由于APF-MMPF算法中粒子采样利用了当前量测信息,可有效提高对机动目标的检测跟踪性能。

To solve the problem of maneuvering target detecting and tracking in a low SNR environment, a Track Before Detect （TBD） algorithm combining the auxiliary particle filtering with multiple-model particle filtering （APF-MMPF） was studied. Predict particles in the multiple-model particle filtering process, which contained target existence and moving mode variables, were directly used to produce auxiliary variable for conducting auxiliary particle filtering and realizing the detection and tracking of maneuvering target. It could be seen from the simulation result of APF-MMPF algorithm and pure MMPF algorithm that： with higher detection probability and smaller tracking error, the detecting and tracking performance of APF-MMPF algorithm was superior to that of MMPF algorithm. Due to the usage of current measurements in the process of sampling particles, the APF-MMPF algorithm can improve the performance of detecting and tracking the maneuvering target.