Estimation method for sensor system error based on Markov stochastic jump system

In order to resolve the problem of system error in a Markov stochastic jump system, this paper proposes a novel on-line system error estimation method based on Markov chain Monte Carlo (MCMC) and maximum likelihood. It uses a Metropolis-Hastings sampler to sample from an equitable probability density distributing function which is based on the maximum likelihood estimation. Besides, it can iteratively estimate system error by using expectation maximization (EM) based on the causation of system error estimation and state estimation. The paper simulates two scenes which include time-varying and time-invariant system errors, and the simulations show that this method can take into consideration the system error statistical characteristics, and is feasible and effective in estimating system errors to solve the case of the unknown target state model.