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Position: Home > Articles > Intelligent Vehicle Multi-objective Tracking Based on Particle Swarm Algorithm Forest Engineering 2020 (4) 70-75

基于粒子群算法的智能车辆多目标跟踪

作  者：
孙柱;赵强;张娜;朱宝全;王娜
单  位：
关键词：
智能车辆;多目标跟踪;多假设跟踪;卡尔曼滤波;粒子群算法
摘  要：
针对智能车辆在公路行驶过程中对其他运动车辆的实时跟踪问题,提出基于离散二进制粒子群算法的多目标物体跟踪方法。本文通过基于雷达数据建立跟踪目标的状态方程和测量方程,将雷达多个扫描周期的测量数据进行关联,形成含有可能轨道的多种假设,利用似然率对轨道进行评分,形成混合整数线性规划问题,并采用离散二进制粒子群算法进行求解。解决各个时刻测量值和目标的关联问题,实现对跟踪目标的轨道判别。最后通过在MATLAB中进行仿真,验证了算法的有效性。
译  名：
Intelligent Vehicle Multi-objective Tracking Based on Particle Swarm Algorithm
作  者：
SUN Zhu;ZHAO Qiang;ZHANG Na;ZHU Baoquan;WANG Na;Traffic College,Northeast Forestry University;Heilongjiang University of Science and Technology,Institute of Electrical and Control Engineering;
关键词：
Intelligent vehicle;;multi-objective tracking;;multi-hypothesis tracking;;Kalman filtering;;particle swarm optimization
摘  要：
Aiming at the problem of simultaneous tracking of other vehicles in the process of intelligent vehicles driving on the road, a multi-object tracking method based on binary particle swarm optimization is proposed. In this paper, the state equation and measurement equation of tracking target are established based on radar data, the measurement data of multiple scanning periods of radar are correlated to form a variety of assumptions with possible orbit, and the orbit is scored by likelihood ratio to form a mixed integer linear programming problem, which is solved by discrete binary particle swarm optimization algorithm. The problem of correlation between the measured value and the target at each time is solved, and the track discrimination of the tracking target is realized. Finally, through the simulation in MATLAB, the data results verify the effectiveness of the algorithm.

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