针对复杂环境下自主水下航行器(AUV)组合导航系统中存在的模型不完全确定或者模型参数发生变化的情况, 提出一种基于期望模式修正的交互式多模型(EMA-IMM)滤波算法。该算法利用滤波估计过程中所得到的模型概率完成决策。首先对固定结构的基础网格进行滤波, 得到细化的修正模型集, 接着对修正模型集进行滤波, 得到与真实模型最为邻近的若干个修正模型网格共同构成的期望模型集, 然后将系统真实的模型覆盖在精简的期望模型集范围之中, 最后通过对期望模型集滤波, 得到接近真实模型状态变量的估计结果。在AUV组合导航系统中的仿真结果表明, 相对于传统Kalman滤波算法, 改进的EMA-IMM使AUV的经度估计精度提高了97％, 纬度估计精度提高了44%; 相对于IMM算法, AUV的经度估计精度提高了22%, 纬度估计精度提高了19%; 得到的结果验证了提出的EMA-IMM算法的优越性。

An Interactive Multiple Model (IMM) filter algorithm based on Expected-mode Augmentation (EMA) named EMA-IMM algorithm is proposed to overcome the uncertain model and time-varied model parameters of the integrated navigation system for an Autonomous Underwater Vehicle( AUV) in a tough environment. The EMA-IMM algorithm mainly uses the probability of models obtained from the recursive estimate processing for making decision. It filters for the base grids of fixed structure to obtain a fined amendatory model set firstly. Then the amendatory model is filtered to obtain an expected model consisting of a small number of amendatory model grids that are close to the real model. Through a further filtering using the expected model, the suboptimal solution approximate to the real model will be ultimately achieved. Simulation results on the integrated navigation system show that the EMA-IMM algorithm can improve the estimation precisions of longitude and latitude by 97％and 44% respectively as compared with the Kalman filtering algorithm and by 22% and 19% with the IMM algorithm, which proves the superiority of the proposed EMA-IMM algorithm.