高斯混合概率假设密度滤波的改进与应用研究

摘要

In Gaussian mixture probability hypothesis density (GM-PHD)filter,the new born targets exist the whole detection domain,and the specific position is hard to define for occurring randomly.Therefore,an improve algorithm,using the received measurements adaptively generate target intensity function to record the surviving target intensity function,is realized.The algorithm can adaptively distinguish the surviving and new born target intensity function to improve the accuracy.Multiple target position tracking simulation and the dol-phin whistle real data processing are used to test improved algorithm performance,and optional sub pattern as-signment (OSPA)function works as a benchmark.The result shows that the proposed algorithm improves the target number estimation and tracking accuracy.The correct rate of target number estimation is 97%,and the OSPA distance is decreased nearly 30% of the original GM-PHD filter.%针对高斯混合概率密度（Gaussian mixture probability hypothesis density，GM-PHD）滤波器存在新生目标在整个检测区域随机出现位置难以确定的问题，实现了一种基于量测驱动目标新生概率密度函数算法，每个扫描周期接收到新的量测信息自适应生成目标强度函数，记录存活目标强度函数，从而实现自适应区分存活目标的强度函数和新生目标，提高算法精度。利用多目标位置追踪仿真数据以及实测海豚哨声信号频率对算法进行了测试，最优子模式分配函数（optional sub pattern assignment，OSPA）作为算法监测标准，结果证明了新算法在目标数目估计以及追踪精度方面都有明显的改善，目标数目估计正确率达到97％，OSPA 距离较 GM-PHD 算法下降30％。