基于IMM的改进去偏转换测量目标跟踪算法

An IMM Based Modified De-Biased Converted Measurement Target Tracking Algorithm

针对机动目标跟踪,提出一种基于交互式多模型的改进去偏转换测量卡尔曼滤波算法(IMM-MDCMKF),该算法在多模型中使用了改进的去偏转换测量卡尔曼滤波算法(MDCMKF)。MDCMKF算法先通过引入状态估计值对转换误差协方差进行修正,有效地降低了测量噪声对此协方差的影响,然后将修正的转换误差协方差用于目标跟踪问题中的去偏转换测量卡尔曼滤波算法。最后进行的Monte Carlo仿真结果表明,所提算法跟踪精度优于IMM-EKF算法和IMM-DCMKF算法。

To the issue of maneuvering target tracking,a modified de-biased converted measurement Kalman filter was proposed based on Interacting Multiple Model （IMM-MDCMKF）,which used a Modified De-biased Converted Measurement Kalman Filter （MDCMKF） in multi-model.The MDCMKF algorithm improved the converted error covariance matrix by introducing state estimated value,and effectively reduced the effect of measurement noise on the covariance.Then the modified converted error covariance matrix was utilized to implement the DCMKF algorithm for a target tracking scenario.Finally,Monte Carlo simulation was carried out,and the results show that the accuracy of algorithm is superior to that of the IMM-EKF algorithm and IMM-DCMKF algorithm.