混合估计多模粒子滤波的机动弱小目标检测前跟踪算法

针对低信噪比条件下的机动小目标实时检测与跟踪困难的问题, 提出一种基于混合估计多模粒子滤波的检测前跟踪改进算法. 首先根据前一时刻所采用的模型状态及其转移概率等先验信息实现当前时刻的模型采样; 然后在充分考虑当前量测下实现当前的粒子预测, 采用一种序贯重要性平滑重采样策略, 在不增加计算量的前提下, 改善了粒子多样性衰退的问题; 最后通过新的粒子集完成对模型和状态的合理估计与目标检测. 仿真结果验证了该方法的检测与跟踪性能优于传统的多模粒子滤波方法.

Track-before-detect for maneuvering weak target based on mixture estimation of multi model particle filter algorithm

For the problem of maneuvering target detection and tracking in low signal-to-noise environment, the improved track-before-detect algorithm based on mixture estimation of the multiple model particle filter is presented. Firstly, the current moment model is sampled by the previous moment particle model information and the model transfer probability. Then, the prediction for current particle is done by current measurement information, and the sequential importance resampling smoothing way is presented, which can effectively increase particle diversity without increasing account task. Finally, the model and state are effectively estimated by the new particle muster. Simulation results show that the proposed algorithm has better performance of detection and tracking compared with the standard multi model particle filter.