基于多驻留测量的相控阵雷达目标跟踪算法

针对相控阵雷达目标跟踪和资源调度问题,给出了基于后向概率的后向递归概率数据关联滤波(PDAF),以及基于后向概率最大的多维数据关联(N-D)方法。推导了预测位置偏离波束指向、且误差是相关的情况下,估计目标平均信噪比和目标检测概率的方法。通过仿真,比较了不同测量窗口数据长度和不同杂波环境下,相应算法对目标维持跟踪的性能和雷达能量需求。仿真结果表明,基于后向递归的PDAF优于常规的PDAF和N-D方法;虽然基于后向概率最大的N-D方法比常规的N-D方法好,但当维数较少时仍然比PDAF性能要差。

Phased-array radar target tracking algorithms based on multi-dwell measurements

For phased-array radar target tracking and resource scheduling, two algorithms based on multi-dwell measurements are presented. One is backward recursive probabilistic data association filtering(PDAF),and the other is maximum retrodicted probability-based n-dimensional(N-D) association.The methods for estimating expected signal-to-noise ratio(SNR) and detection probability when prediction position errors are correlated and biased from beam position are also presented.Considering the target maintainability,comparisons are made through simulations with different number of dwells and clutter density.The results show that the back-ward recursive PDAF is better than the standard PDAF and N-D algorithms.Although maximum retrodicted probability-based N-D is better than the conventional N-D,it cannot compete with PDAF when N is not large(enough.)