Optimal state estimation for equality‐constrained systems with complex noise

Citations Over TimeTop 14% of 2018 papers

Abstract

Summary Accurate system description is important to obtain accurate state estimation for many application areas. Much a priori information and complex noise pollution exist in real systems and impact the accuracy of system descriptions. Thus, this paper focuses on the state estimation problem with prior information for systems with complex noise pollution. Considering the additional prior information about the state with constraint increase, the estimate quality is compared to classical state estimation methods, which cannot utilize the prior information. Therefore, linear systems with complex noise include additive noise and multiplicative noise, which are taken into account, and an optimal algorithm is deduced. For nonlinear systems and non‐Gaussian complex noise, an algorithm is proposed based on variational Bayesian inference to address this general problem. Furthermore, an algorithm comparison, a performance analysis, and a convergence analysis are presented. The estimation performance is illustrated in numerical target tracking examples.

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