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

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

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。

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