Markov随机跳变系统的传感器系统误差估计

Abstract

Abstract: 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.

Key words: system error estimaiton, maximum likelihood estimation, Markov chain Monte Carlo, Metropolis-Hastings sampling, expectation maximization

CLC Number:

V219
TN953

ZHOU Lin, PAN Quan, LIANG Yan. Estimation Method for Sensor System Error Based on Markov Stochastic Jump System[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2012, (6): 1070-1076.

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Estimation Method for Sensor System Error Based on Markov Stochastic Jump System

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