基于改进扩展卡尔曼滤波算法的空中目标跟踪

采用扩展卡尔曼滤波方法建立了雷达跟踪模型，对空中目标航迹进行滤波，为了减少雷达量测噪声的不稳定变化对系统跟踪性能的影响，对扩展卡尔曼滤波算法进行了改进，利用新息方差的计算来调整卡尔曼滤波器的增益。仿真结果表明，采用改进扩展卡尔曼滤波算法后，在雷达量测噪声发生大幅变化的情况下，经过滤波后的位置和速度误差仍然趋于稳定。表明该方法具有很好的滤波性能及跟踪精度，并可以提高空中目标航迹预测的精确性。

Air Target Tracking Based on Improved Extended Kalman Filtering Algorithm

This paper uses extended Kalman filtering （EKF） method to set up the radar tracking model to filter the air target flight track,in order to reduce the influence of instable variation of ra- dar measurement noise on system tracking performance,improves the EKF algorithm, uses the cal- culation of innovation covariance to adjust the Kalman filter gain. The simulation results show that the position and velocity error still be stable even though the measurement noise of radars vary in a wide range after using improved EKF algorithm,which indicates that the method has excellent fil- tering performance and tracking precision,and can improve the accuracy of track prediction for air target.