自适应蚁群算法在多目标跟踪中的应用

The Application of Improvement UKF algorithm in Over-the-Horizon Radar

在多目标状态跟踪估计方法的研究中,多目标数据关联容易陷入局部最优状态以及跟踪精度不高的缺点,提出一种改进蚁群算法的数据关联方法。改进方法为:在传统蚁群算法的基础上,引入标识判定系数来调节信息素浓度的增量,避免某条路径上信息素浓度增长过快,从而陷入局部极值的情况,同时对挥发因子进行自适应控制,保持搜索的平衡性和全局性,避免搜索落入局部最优从而陷入停滞状态。仿真结果表明,改进的蚁群算法在多目标跟踪中,既可有效地避免搜索落入局部最优的弊端,又明显地提高了跟踪精度。

For the tracking accuracy in the data association problem of multiple target tracking is not high enough, this paper proposes a data association method based on improved ant colony algorithm. Firstly, a combinatorial opti- mization model for the data association problem is made , then, it changes the way of updating pheromone concentra- tion in ant colony algorithm, and controls the evaporation factor adaptively to maintain a balance and global search so that it avoids falling in＇~o local optimum and the search stagnation. The simulation results show that the improved ant colony algorithm can make the data association accurately, and obviously improve the precision of the association in multiple target tracking.