一种基于粒子滤波的双极化雷达检测前跟踪算法

The algorithm of track before detect using particle filter for dual-polarized radar

针对雷达在低信噪比(signal to noise ratio,SNR)条件下对运动目标的检测和跟踪难题,提出了一种基于粒子滤波(particle filter,PF)的双极化雷达运动目标检测前跟踪(track before detect,TBD)算法,又称联合粒子滤波检测前跟踪(joint particle filter-track before detect,JPF-TBD)方法.该算法借鉴传统的TBD算法处理框架,以经典PF算法为基础,使用双通道幅度相位似然比函数计算粒子权值,并实现了完整的PF过程.与同类研究相比,所提算法能够充分利用双极化雷达各通道幅度和相位信息,进一步扩展了PF算法的应用范围.仿真实验表明:在SNR>10 dB,虚警概率为10-6的情况下所提算法对目标的检测概率大于0.8.

On the condition of dual-polarized mode,the radar may suffer from the noise and the clutter,and it poses a significant challenge to detecting and tracking weak targets.To address this problem,a novel joint particle filter algorithm,which can handle dual-polarized data of weak target,is proposed.The algorithm is prepared from the framework of particle filter track before detect(PF-TBD)filter,and it is implemented by firstly adopting a dual-polarized likelihood ratio function(LRF),which can greatly improve the performance of PF-TBD.Compared with classic method,the new approach combines the dual-polarized data with PF-TBD,which provides a new way to such problems,avoiding the loss tracking of the weak target with lower signal to noise ratio(SNR).When SNR>10 dB and the false alarm probability is less than 10-6,the target detection probability can be above 0.8.