基于消隐记忆SCKF的交互式多模型算法

摘要

针对交互式多模型算法(Interacting Multiple Model,IMM)在机动目标跟踪时,因模型不准确导致的滤波误差增大问题,提出了基于消隐记忆平方根容积卡尔曼滤波(Memory Attenuation Square Root Kalman Filter,MASCKF)的交互式多模型算法(IMM-MASCKF).该算法在模型滤波中引入消隐记忆滤波理论,通过消隐记忆因子增大新息在滤波中的比重,改善了滤波器对目标机动的动态性能,提高了滤波精度.仿真结果表明,该算法可以实现对机动目标的有效跟踪,且与常规交互式多模型算法相比减小了滤波误差.%In order to decrease filtering error caused by inaccurate model when tracking maneuvering target by interacting multiple model(IMM) algorithm ,an IMM algorithm based on memory attenuation square root Kal-man filter (IMM-MASCKF) was proposed .Attenuation memory filtering theory was applied in the filtering , and the proportion of new measurement in filter was increased through the attenuation memory factor ,and then the dynamic performance for maneuvering target tracking and filtering precision was improved .Simulation re-sults showed that the proposed algorithm was effective for maneuvering target tracking ,and the filtering error was reduced in comparison with conventional interactive multiple model algorithm .