改进PDA-AI方法的运动目标跟踪性能分析

为了解决概率数据关联法(PDA-AI)的信号模型与光电探测跟踪系统不符的实际问题,提出了采用目标信号幅度连续性和运动轨迹一致性进行运动分析的改进PDA-AI(MPDA-AI)。该方法利用目标信号幅度在短时间内变化缓慢,相关性强的特点,运用一阶马尔可夫模型描述目标的运动信息和幅度信号,分析量测点的运动信息和幅度信号关联过程,并详细计算和讨论典型密集杂波环境下PDA-AI和MPDA-AI的Cramer-Rao估计误差下界。理论分析和试验结果表明,MPDA-AI估计出的目标状态较PDA-AI更加准确,可信程度更高,能更进一步提高目标检测跟踪的可靠性。

Performance analysis on improved PDA AI for moving target tracking

Aim at the problem that the EO imaging tracking system is inconsistent with the model of probabilistic data association with amplitude information (PDA AI), which supposes that the greater the amplitude value is, the greater the probability of being the tracked target will be, a modified PDA AI (MPDA AI) is presented . Based on the fact that the amplitude and the motion of the interested target are consistent in a short period, the MPDA AI models the amplitude information and the motion information of the target as well as their consistency with Markov stationary signal to analyze the association procedure of motion and amplitude. The lower bounds of Cramer Rao estimation error for PDA AI and MPDA AI are calculated and discussed in detail. The theoretical analysis and experimental results show that estimating the target motion with the MPDA AI will be more accurate and more reliable than estimating with the original PDA AI.