ALGORITHM OF IMM COMBINING KALMAN AND PARTICLE FILTER FOR MANEUVERING TARGET TRACKING

摘要

The particle filter can deal with nonlinearon-Gaussian problems and it has been introduced to the algorithm of the interacting multiple model (IMM) for higher precision. The general IMM based on Kalman filter or extended Kalman filter (IMMEKF) cannot deal with non-Gaussian problems and also does not work as well as the IMM based on the particle filter (IMMPF) for the nonlinear problems. However the problem of the particle filter is its expensive computation, because a particle filter usually has a lot of particles, which will increase the computation load greatly. Here an algorithm of IMM combining the Kalman filter and the particle filter (IMMK-PF) for maneuvering target tracking is proposed to improve the computation efficiency as compared to the IMMPF. For nonlinear/Gaussian problems the new algorithm is expected to have a good performance as the IMMPF, while for linear problems it will perform similarly to the IMMEKF and will work better than the IMMPF.