一种新的可变数目机动目标联合检测与跟踪算法

New method for joint detection and tracking with variable maneuvering target number

在低信噪比多传感器观测环境下,针对机动目标数目变化时跟踪性能不高的问题,提出一种新的目标联合检测与跟踪算法。依据粒子存在变量进行预测状态粒子集的采样,考虑状态粒子集与当前观测值的关联程度,利用模糊拍卖算法与粒子群优化理论解决状态集与观测集之间的关联问题,并给出目标出现与消失的判定准则,实现粒子权值更新;依据混合采样方法得到包含模型信息和状态信息的粒子集,并按照目标模型概率进行粒子状态融合得到目标局部状态后验估计值和均方误差;最后,对关联的各传感器局部航迹信息进行加权融合,得到各目标的全局状态估计值,并与经典多模粒子滤波算法的仿真试验对比分析。研究结果表明:新算法在运动模型概率估计、状态估计及目标数目估计方面具有有效性。

Aiming at the problem of low target tracking performance with the variable number of maneuvering target, a new joint detection and tracking algorithm was put forward for the environment of low signal-to-noise ratio multi-sensor observation. The sampling of predict state particle set was completed according to the existence of the particle. And then, taking the association degree of the sets of particle state and the current observations into account, and using the theory of fuzzy auction algorithm and particle swarm optimization, the association problem between the state and observation sets was solved, and the criteria of target appear and disappear was given, and the updating of particle weight was realized. By the means of composite sampling, the sample particle sets with the model information and status information was obtained, and the local posteriori estimate and the mean square error of target state were given with the target model probability through particle state fusion. Finally, with the weighted fusion of each associated local sensor tracking information, the global state estimation of each target was obtained and compared with the simulation experiment with classical multiple model particle filter algorithm. The results show that the new algorithm is effective in motion model probability estimation, state estimation, and the target number estimation.