基于RUKF-IMM的非线性系统滤波

Filtering of nonlinear systems with measurement loss by RUKF-IM M

从交互式多模型估计(IMM)方法的特点出发,提出用IMM估计方法对有测量数据丢失的非线性系统进行估计.IMM模型集中包含两个模型:一个模型对应测量数据丢失情况,另一个对应测量数据未丢失.最终基于两个模型的估计进行融合得到估计结果,改善估计器在测量信息丢失情况下的稳定性.采用随机无迹卡尔曼滤波(RUKF)方法对每个模型分别进行滤波,消除标准无迹卡尔曼滤波(UKF)方法的系统误差.仿真结果表明:在测量信息丢失的情况下,提出的估计方法在稳定性与估计性能上都优于传统的基于单模型的非线性系统混合估计方法.

From interacting multiple model（IMM） estimation,the s tates in nonlinear systems with measurement loss were estimated by IMM approach. The model set in the IMM approach contained two models,one of them corresponde d to the situations with measurement loss and the other one corresponded to the situations without measurement loss.Final estimates were obtained based on the fusion of the two model estimates.This approach improved the stability of the e stimator in systems with missed measurements.Each filter in IMM used the random ized unscented Kalman（RUKF） to estimate the states.RUKF eliminated the system errors caused by the unscented Kalman fitler（UKF）.Thus the accuracy of the est imation was also improved.The simulation results show the proposed approach is more stable and accurate than the traditional one-model mixture estimation appr oaches in nonlinear systems with measurement loss.