跳变约束下马尔可夫切换非线性系统滤波

For multi-mode uncertainty of system state evolution and diversity of state constraints, an interactive multihypothesis estimation method for Markov switched systems with jump constraints is proposed. The hypothesis set containing the possible values of the jump Markov parameter is defined. According to the optimal Bayesian filtering, the recursive update of the posterior probability of the state and hypothesis is derived. Based on statistical linear regression, the pseudo measurement method is used to extend the linearized constraint to the real measurement, and the approximate analytical optimal solution of the nonlinear system filtering is given. Finally, an approximate optimal estimation of the sparse grid integral algorithm is presented. In the simulation scenario of crossing road maneuvering target tracking, the filtering accuracy of the proposed algorithm is better than that of the interactive multi-model algorithm based on Taylor expansion, the interactive multi-model algorithm based on statistical linear regression, and the constrained filtering algorithm for nonlinear systems based on Taylor expansion.