基于速度方位约束的多传感器模糊数据互联

针对杂波环境下多传感器跟踪多目标的问题,提出了一种基于速度方位约束的多传感器模糊数据互联算法(multi-sensor fuzzy data association method based on velocity and azimuth, VA-MSFDA)。该算法首先利用方位速度信息对确认区域内的有效量测作进一步筛选,剔除部分虚假量测,然后基于模糊聚类方法计算候选量测与观测区域内各目标互联的概率,应用顺序结构多传感器联合概率数据互联(multi-sensor joint probabilistic data association algorithm,MSJPDA)原理,依次处理各传感器中的目标测量数据,实现对多目标的跟踪。仿真结果表明,与顺序MSJPDA相比,VA-MSFDA在算法耗时、估计精度、收敛速度和量测正确关联率等方面优势明显,能够更好地解决杂波环境下的多目标跟踪问题。

Multi-sensor fuzzy data association based on velocity and azimuth

To deal with the problem of multi-sensor tracking multi-target in a cluttered environment, a novel multi-sensor fuzzy data association method based on velocity and azimuth (VA-MSFDA) is proposed. Firstly, the validated measurements are selected based on course and velocity information, and some false measurements are eliminated. Then the association probabilities between candidate measurements and targets are calculated on the basis of fuzzy clustering. Finally, the selected target’s measurements from different sensors are dealt with on the basis of sequential multi-sensor joint probabilistic data association (MSJPDA) algorithm, and the target’s estimation is obtained. Simulation results show that VA-MSFDA outperforms the sequential MSJPDA algorithm in the aspects of time consumption, tracking accuracy, convergence rate and correct association probability, which can be considered as a better method to solve the multitarget tracki-ng problem.