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根据目标在运动过程中位置与速度只能连续变化这一事实,提出了杂波环境下基于跟踪微分器的一种多目标数据关联算法.算法利用跟踪微分器得到目标波门内所有量测的位置与速度,通过将其与目标前一时刻的位置和速度的比较来实现在未知杂波环境下的多目标数据关联.该算法直接利用量测数据,不需要目标运动、传感器噪声及杂波的先验统计知识,在目标数已知杂波不很密集的情况下具有良好的数据关联能力.此算法计算量小、结构简单,与目标状态滤波估计算法完全分离,便于模块化设计和与其他滤波算法结合,易于工程实现.仿真结果验证了算法的有效性.
According to the fact that the position and velocity can only change continuously during the movement, a multi-objective data association algorithm based on tracking differentiator in clutter environment is proposed. The algorithm uses the tracking differentiator to get all the measurements Position and speed of multi-target data correlation in unknown clutter by comparing its position and speed with the previous moment of the target.The algorithm directly uses the measured data without the need of target motion, sensor noise and clutter The prior knowledge of a priori statistical knowledge, in the case of the target number of known clutter is not very dense with good data-related ability.This algorithm has a small amount of calculation, simple structure, complete separation from the target state filtering estimation algorithm, to facilitate the modular design and The combination of other filtering algorithms is easy to implement and the simulation results verify the effectiveness of the algorithm.