强跟踪滤波算法由于对判断滤波发散的阈值设置较小，以较大概率产生渐消因子而导致对滤波增益过调节，最终对状态估计不够平滑. 在分析强跟踪滤波算法运行机理的基础上，提出了一种改进的强跟踪滤波算法. 通过适当提高判断滤波发散的阈值，有效降低了误判滤波发散的概率，并能针对不同维数量测方程确定不同的弱化因子，避免了凭经验加入弱化因子解决这一问题的缺陷. 数值仿真结果表明：改进的强跟踪滤波算法对系统状态突变不但具有较强的鲁棒性，而且能有效保持滤波精度和对状态估计的平滑性，从而验证了该算法的可行性和有效性.
Strong tracking filtering (STF) sets small threshold to judge filtering divergence leading to fading factor with high probability, which causes excessive regulation of the filtering gain and makes the state estimation curve lack smoothness. By analyzing the operation mechanism of STF, improved STF (ISTF) is proposed. The proposed algorithm reduces probability of misjudging filter divergence by appropriately increasing the threshold. It determines the softening factor to suit different dimensions of the measurement equation, and thus avoids the disadvantages of the previous methods that determine the softening factor according to experiences. Simulation indicates that ISTF can maintain filtering accuracy and estimation smoothness, and is robust against sudden changes in the system state, showing its feasibility and effectiveness.
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