【摘要】 针对Kalman滤波不能处理多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。通过对量测方程的非线性分析,利用粒子滤波器计算目标状态估计值,通过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和更少的计算量。相比于单传感器,减少了量测信息的模糊性,提高了资源的利用率。更多还原

【Abstract】 Aiming at the restriction of Kalman filter in dealing with nonlinear problems of measurement information in multi-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.It also reduces the fuzziness of measurement information and improves resource utilization compared to the single sensor.更多还原