基于有限差分扩展卡尔曼粒子滤波的多特征自适应融合的目标跟踪算法研究

针对复杂环境下目标跟踪问题,提出了一种基于 有限差分扩展卡尔曼粒子滤波的多 特征自适应融合跟踪算法。采用有限差分扩展卡尔曼滤波器对采样粒子集合进行滤波更新, 通过融入最新观测信息的方法消弱权值退化现象；在新算法的框架内,利用目标静态和动态 互补特征作为观测量,实现不同环境下目标的多特征自适应融合跟踪。实验结果表明,本文 方法具有较好的跟踪精度和抗噪声干扰能力。

Object tracking based on finite-difference extended kalman Filter and multi-feature adaptive fusion

In order to meet the demand of high accuracy and strong robustness of veh icle tracking in the intelligent transportation system,a new adaptive multi-fea ture fusion tracking algorithm is proposed in this paper.The proposed algorithm overcomes the partic le degeneration phenomenon well by using finite-difference extended Kalman filter to produce op timization proposal distribution function. An adaptive multi-feature fusion method is pro posed to overcome the defects of the additive fusion and the multiplicative fusion.The n ew method uses static and dynamic characteristics as complementary observables i n the framework of the improved particle filter.Experimental results show that this method is effective in enha ncing the accuracy and robustness of vehicle tracking system in different environments.