Fusion of interacting multiple model and improved particle filter for underwater bear-ings-only maneuvering target tracking

Aiming at the problems of measurement nonlinearity and model uncertainty in underwater bearings-only maneuvering target tracking, this paper proposes a novel interactive multiple model (IMM) algorithm framework that combines improved particle filters. The framework calculates and updates the model probability corresponding to each particle through Kalman filter and resampling algorithm, which realizes the effective fusion of IMM and improved particle filters (such as EFP, UPF, CPF,etc). Furthermore, to alleviate the phenomenon of particle degradation, we design an adaptive resampling mechanism that integrates Gaussian mutation and crossover evolutionary strategies. Simulation results demonstrate that compared with conventional algorithms, the IMM-HCPF achieves significant improvements in estimation accuracy, convergence speed and real-time performance.