单 位：
甘肃省工业过程先进控制重点实验室;兰州理工大学电气工程与信息工程学院
关键词：
车辆跟踪;多特征融合;有限差分;粒子滤波
摘 要：
提出了一种新的自适应多特征融合跟踪算法。该算法采用多项式近似与中心差分方法实现建议分布函数的优化处理,通过扩展卡尔曼滤波器在采样粒子集中融入最新的量测信息,较好地克服了粒子权重退化问题;同时,为克服乘性与加性融合算法的缺陷,采用自适应多特征融合方法,将目标汽车静态和动态互补特征作为观测信息,在新算法的框架内进行自适应融合跟踪。实验结果表明,该方法有效提升了不同环境下车辆跟踪系统的精确性和鲁棒性。
译 名：
Vehicle Tracking Based on Multi-feature Adaptive Fusion
作 者：
Li Yuchen1,2 Li Zhanming1,2(1.School of Electric Engineering and Information Engineering,Lanzhou University of Technology,Lanzhou 730050,China 2.Key Laboratory of Advanced Control of Industrial Processes of Gansu Province,Lanzhou 730050,China)
关键词：
Vehicle tracking Multi-feature fusion Finite-difference Particle filter
摘 要：
A kind of adaptive multi-feature fusion tracking algorithm was proposed.The proposed algorithm overcame the particle degeneration phenomenon well by using finite-difference extended Kalman filter.The proposal distribution function was optimized.The latest observation information was fused into the suggestion distribution function by using finite-difference extended Kalman filter.Meanwhile,an adaptive multi-feature fusion method was proposed to overcome the defects of the additive fusion and the multiplicative fusion.The proposed method used static and dynamic characteristics as complementary observables in the framework of improved particle filter.Experimental results showed that the proposed method was effective in enhancing the accuracy and robustness of vehicle tracking system in different environments.
