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为了减小传统跟踪滤波算法线性化误差,提高光电跟踪系统的跟踪速度和跟踪精度,本文在三维空间中,提出了二阶去偏转换测量卡尔曼滤波算法。该算法利用二阶泰勒展开的方法,推导出了光电跟踪系统观测方程的转换测量值误差的均值和协方差矩阵表达式,并对测量误差进行去偏差补偿处理,再经过转换测量卡尔曼滤波,可显著减小传统滤波算法的线性化误差。仿真结果表明,二阶去偏转换测量卡尔曼滤波(SCMKF)算法的跟踪精度优于非去偏转换测量卡尔曼滤波(CMKF)和扩展卡尔曼滤波(EKF),以及unscented卡尔曼滤波(UKF)算法,并且具有更快的收敛速度,和采用统计方法的去偏转换测量卡尔曼滤波(DCMKF)的跟踪精度相当,但计算简单,提高了跟踪速度。
In order to reduce the linearization error of the traditional tracking filtering algorithm and improve the tracking speed and tracking accuracy of the electro-optical tracking system, this paper proposes a second-order de-skew measurement Kalman filtering algorithm in three-dimensional space. The algorithm uses the method of second order Taylor expansion to derive the mean and covariance matrix expression of the conversion measurement error of the photoelectric tracking system observational equation. The error of the measurement is compensated by de-skew compensation. After the conversion Kalman filtering, Can significantly reduce the linearization error of the traditional filtering algorithm. The simulation results show that the tracking accuracy of second order de-skew measurement Kalman filter (SCMKF) algorithm is better than that of CMKF and EKF and unscented Kalman filter (UKF) Algorithm, and has a faster convergence rate. Compared with the DCMKF, a statistical method is adopted to achieve the same tracking accuracy, but the calculation is simple and the tracking speed is improved.