在经典的交互式多模型算法中，对似然函数的高斯近似以及概率密度函数与概率质量函数的混合计算使得所求得的模型概率仅为贝叶斯意义下的次优值．为解决此问题，基于各滤波器估计误差的相关性和多传感器最优信息融合准则，提出了一种重新加权的交互式多模型算法．该算法通过计算估计误差的互协方差阵对模型概率进行更新，在此基础上利用最优信息融合理论对各滤波器的滤波结果进行融合．理论分析及仿真结果表明: 经过重新加权的交互式多模型算法较原始算法以及其他忽略误差相关性的交互式多模型的改进算法在估计精度上均有显著的提高．

In the classical interacting multiple model (IMM) algorithm,because of the Gaussian approximation tothe likelihood function and the confusion of probability density function and probability mass function,the obtainedmode probabilities are only the approximations of probability mass,which is a suboptimal result in the sense ofBayes.In order to solve this problem,a reweighted IMM algorithm is proposed based on the correlation among theestimation errors of mode- conditioned filters and the multi- sensor optimal information fusion criterion.In this algo-rithm,the mode probabilities are updated by calculating the cross- covariance matrix of estimation errors,and thenthe filtering results are fused according to the optimal information fusion theory.Theoretical analysis and simulationresults indicate that the estimation accuracy of the proposed algorithm is significantly improved in comparison withthose of the classical IMM algorithm and other IMM- related algorithms which ignore the error correlation.