光电跟踪目标的非线性滤波算法研究

Research on Nonlinear Filtering to Optoelectronic Target

推广卡尔曼滤波算法由于忽略线性化误差,当目标距离超过一定限度时,导致跟踪精度迅速下降,易发散。为此,提出了一种以改进跟踪性能的滤波算法。该算法是基于对实际测量噪声方差的正确估计。将距离测量值的非线性作为附加的噪声,进行补偿,克服了非线性的影响,距离测量误差的方差每一步重新估计。仿真结果表明本文提出的算法优于推广卡尔曼滤波算法。

The usual tracking filter design on first-order approximation leads to poor convergence and erratic filter behavior in highly nonlinear situations. A new filtering algorithm for improving the tracking performance is developed based on the fact that correct evaluation of the measurement error covariance is possible. The correct evaluation of the measurement error covariance is obtained by evaluating inherent nonlinearitys of measurements. The variance of range measurement errors is evaluated in an adaptive manner. Simulation results show the proposed algorithm offers superior performance in comparison to the EKF.