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中文摘要：
针对扩展卡尔曼滤波器（EKF）在系统模型不确定时存在鲁棒性差、精度低的问题,设计了一种基于交互式多模型（IMM）的自适应融合滤波（AFF）算法。IMM-AFF算法采用两个模型来描述系统结构,且与每个模型相对应的Sage-Husa滤波器和强跟踪滤波器（STF）独立并行工作,系统的状态估计则是两种滤波器估计的模型概率加权融合。IMM-AFF算法兼具Sage-Husa滤波器状态估计精度高和STF对系统模型不确定具有强鲁棒性的优点,克服了两种滤波器各自单独使用时的缺点。将IMM-AFF算法应用于INS/GPS组合导航系统的仿真结果表明,IMM-AFF算法的滤波精度和鲁棒性均明显优于目前工程应用中的EKF,特别是大大提高了INS/GPS系统的定位精度。
英文摘要：
A new adaptive fusion filtering（AFF） algorithm based on interactive multiple models（IMM） is put forward to solve problems of bad robustness and low accuracy,existing in extended Kalman filter（EKF） when the system model includes uncertainties.In the IMM-AFF algorithm,the system structure is described by two models,and a Sage-Husa filter corresponding to the one and a strong tracking filter（STF） corresponding to another work in parallel independently.The state estimation of system is the weighted fusion of the two filters by using model probabilities,so that the merits of SageHusa filter and STF are combined and their demerits are overcome through AFF.Consequently,the proposed IMM-AFF algorithm shows robustness against model uncertainties and high state estimation accuracy.This fusion filter is applied in an INS /GPS integrated navigation system.Furthermore,simulation results under various error environments show that IMMAFF algorithm is superior to EKF in estimation accuracy and robustness,especially positioning accuracy.
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