一种基于雷达和红外的粒子滤波融合跟踪方法

Tracking Algorithm Based on Radar and Infrared Sensor Particle Filter Fusion

针对Kalman滤波不能处理雷达与红外传感器量测信息融合中的非线性问题,提出了一种基于粒子滤波方法的融合跟踪算法。该算法通过利用量测方程的非线性分析和粒子滤波器计算状态估计值,从而以线性迭代的方式得到系统的最优估计。仿真结果表明,与采用Kalman滤波的方法相比,该算法具有更高的估计精度,同时减小了计算量。

Aiming at the restriction of Kalman filter in dealing with nonlinear problems of measurement information in radar and infrared sensor data fusion, a fusion tracking algorithm based on particle filter is proposed. It uses particle filter to calculate state estimated values by the non-linear analysis of measurement equation, and then the system optimal estimation is obtained in the linear iterative way. The simulation results show that compared with the Kalman filter, the proposed algorithm improves the estimation accuracy and reduces the computational complexity.