Sequential Fusion Filter for Nonlinear Multi-Sensor System with Stochastic Parameter Perturbations, Fading Measurement and Correlated Noise

This paper studies sequential fusion filters for nonlinear multi-sensor systems with stochastic parameters perturbations, fading measurement and correlated noise. Stochastic parameter perturbations occur in the state model and are represented by Gaussian multiplicative noise. The fading measurement phenomenon of each sensor is represented by independent random variables with known statistical characteristics. Moreover, the measurement noises of different sensors are correlated with each other and are correlated with the system noise at the same time. Firstly, we convert multiplicative noise into additive noise and establish a more compact model equivalent to the original system. Secondly, based on the sequential fusion estimation of system noise, measurement noise and innovation analysis methods, a state sequential fusion filter state estimator is derived to deal with the problems studied in this paper, where the decorrelation of correlated noises is avoided. Then, according to the third-degree spherical-radial cubature rule, the numerical implementation steps of the sequential fusion filter are given. Finally, simulation experiment verify the effectiveness of the sequential fusion filter algorithm proposed in this paper.