针对现有飞蛾扑火优化算法精度低、全局搜索能力差的问题,提出一种自适应插值飞蛾扑火优化算法,并将其嵌入多特征粒子滤波中优化,构建自适应插值飞蛾扑火优化的多特征粒子滤波车辆跟踪算法.首先,在飞蛾的位置更新机制中加入自适应权值,改善所提算法的全局搜索能力.其次,采用改进的插值飞蛾扑火优化算法对采样过程进行优化,结合多特征自适应融合优化粒子滤波车辆跟踪算法,根据最新观测信息不断调整粒子分布,使低权值层粒子向权值较高的区域移动,增强粒子质量,避免样本退化.实验结果表明,本文算法能够有效降低状态预测所需的样本粒子数,提高算法的跟踪性能,在车辆目标发生遮挡、光照、姿态及尺度变化等干扰下仍然能够准确、稳定地跟踪目标车辆.

In order to solve the problem of low accuracy and the poor global searching ability of the moth-flame optimization algorithm, an improved adaptive interpolation moth-flame optimization algorithm is proposed, which is embedded into multi-feature particle filter to optimize. Besides, a multi-feature particle filter vehicle tracking algorithm based on adaptive interpolation moth-flame optimization is constructed. First, adaptive weights are added to the moths’ position updating mechanism to improve the global searching ability of the proposed algorithm. Next, the adaptive interpolation moth-flame optimization algorithm is used to optimize the sampling process. Then, in combination with the multi-feature adaptive fusion particle filter vehicle tracking algorithm, the particle distribution according to the latest observation information is continuously adjusted, so that the particles in the low weight layer can move to the area with higher weight to enhance the particle quality and avoid sample degradation. The experimental results show that the proposed algorithm can effectively reduce the number of sample particles required for state prediction, improve the tracking performance of the algorithm, and track the target vehicle accurately and stably under the interferences of occlusion, illumination, attitude, and scale changes.