基于跟踪微分器的多目标数据关联算法

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.

Multi-target data association algorithm based on tracking-differentiator

From the fact that position and velocity of a moving object can not vary discontinuously, a data association algorithm based on tracking-differentiator was presented for multi-target tracking in unknown clutters. The tracking-differentiator was applied to obtain positions and velocities corresponding to the measured data in target gates and multi-target data association in unknown clutter environment was realized by comparing these positions and velocities with target position and velocity at the previous moment. Since the algorithm utilizes measured data directly and does not depend on the prior statistics knowledge about target movements, sensor noise and clutter properties, it has good capability of data association in the environment with lower dense clutter and known number of targets. The algorithm possesses low computing burden and simple structure, and separates with target state filtering algorithms completely, which facilities modular design, combination with other filtering algorithms and implementation in practical engineering. Simulation results illustrate the effectiveness of the proposed algorithm.