Abstract:In order to improve the tracking ability of the surface maneuvering target,the surface target was modeled as an elliptical area,and an interacting multiple model (IMM) strong unscented particle filter based on fuzzed model probability was proposed.Firstly,a measurement equation of the elliptical area target was derived by using the extended area target measurements of modern radars.Then,the IMM-SUPF was obtained by introducing the strong unscented particle filter (SUPF) into IMM.Laterly,the strong tracking unscented Kalman filter (STUKF) was utilized to generate the proposal distribution in SUPF,and a fading factor was provided in STUKF for adjusting the proportion of state-model covariance and measurement-model covariance,making the proposal distribution more consistent with the posterior probability distribution of true state and the estimation precision of the sub-filters for IMM algorithm higher.At last,the model probabilities of particles were fuzzed by the proposed fuzzy membership function.In this way,the model probabilities of particles for filter with true target model was higher while the model probabilities of particles for the incompatible filters was lower.Accordingly,the fusion accuracy of the IMM algorithm was improved.Monte-Carlo simulation results demonstrated that the tracking precision of IMM algorithm for the area target was higher than that of IMM algorithm for the particle target,and the proposed IMM algorithm had faster convergence speed and slighter overshoot than the traditional one.Moreover,the developed IMM algorithm provided higher precision than the IMM-UPF for the area target.By modeling the surface target as an elliptical area and designing the fuzzed model probability based IMM-SUPF,the proposed IMM algorithm achieved better estimation performance than the traditional IMM algorithm for the particle target.