关键词: 卡尔曼滤波（KF）, 模型误差, 滤波发散, 抑制发散

Abstract: When Kalman filter algorithm is applied in engineering, if there are variously ?inaccurate reasons in the model, the estimated signals can generate large state estimation error, even filtering divergence, which seriously influences the denoising effect of the filter in the signal process. In order to suppress filtering divergence and improve the filtering accuracy, the paper expounds the phenomenon of divergence, analyzes the main causes of Kalman filtering divergence, namely the model error and calculation error, and summarizes the four types of commonly used method of suppressing filtering divergence, respectively to adjusting gain method, prediction error covariance weighting method, limited memory method and adaptive Kalman algorithm. Simulating and analyzing these methods confirm that they all have their own advantages and diaadvantages. Adjust gain method requires the least amount of calculation, adaptive Kalman algorithm has the highest calculation accuracy and weighting method achieves a good balance between the less amount of calculation and high precision requirment. Limited memory method’s filtering effect is equal to weighting method’s, but calculation of limited memory method is somewhat complex. It has practical guiding significance on how to restrain filtering divergence in the engineering applications.

Key words: Kalman Filter（KF）, model error, filtering divergence, suppress divergence