改进布谷鸟算法优化粒子滤波的多目标跟踪方法

标准粒子滤波器的重采样会造成粒子贫化，影响跟踪系统的精度。为克服这一缺陷，提出了一种改进布谷鸟搜索算法优化粒子滤波的多目标跟踪方法。将粒子作为布谷鸟宿主鸟巢，模拟布谷鸟寻找宿主鸟巢位置的行为，通过全局搜索和局部搜索两个阶段使粒子向高似然区域移动。同时，改进布谷鸟搜索算法的寻优机制，提出动态搜索步长和强化局部搜索的方法，加强了算法的全局搜索的收敛速度。此外，改进算法结合了联合概率数据关联，用于解决多机动目标跟踪问题。本文设置了一维环境和二维环境两组实验，对比优化后的粒子滤波算法与标准粒子滤波算法的目标跟踪性能。实验结果表明，本文提出的算法不仅全局收敛速度更快，而且提高了多机动目标跟踪的精度；与标准布谷鸟搜索优化粒子滤波算法相比，全局收敛迭代速度提高了28.5%；与粒子滤波联合概率数据关联和粒子群优化粒子滤波联合概率数据关联算法相比，估计精度分别提高了24.7%和11.81%。

Multi-object tracking method based on improved cuckoo search algorithm optimized particle filter

Resampling in standard particle filters can lead to particle impoverishment, affecting the accuracy of tracking systems. To overcome this deficiency, an improved cuckoo search algorithm-based optimization method for particle filtering in multi-object tracking is proposed. In this method, particles are treated as host nests for cuckoo birds, simulating the behavior of cuckoo birds in locating nest positions. The algorithm consists of two stages: global search and local search, which collectively guide particles towards high likelihood regions. Furthermore, enhancements are made to the cuckoo search algorithm, introducing dynamic search step sizes and reinforcing the local search mechanism, thereby improving the convergence speed of the algorithm in global search. Additionally, the improved algorithm incorporates joint probability data association for addressing multi-maneuver object tracking problems. Two sets of experiments are conducted in one-dimensional and two-dimensional environments to compare the tracking performance of the optimized particle filtering algorithm with the standard particle filtering algorithm. The experimental results demonstrate that the algorithm proposed in this paper exhibits not only faster global convergence but also an enhanced precision in multi-object tracking. In comparison to the standard Cuckoo Search Optimized Particle Filter algorithm, it showcases a 28.5% increase in global convergence iteration speed. Furthermore, when juxtaposed against the particle filter joint probability data association and particle swarm optimization particle filter joint probability data association algorithms, it shows respective accuracy enhancements of 24.7% and 11.81% in estimation precision.