Distributed state estimation for linear time-invariant dynamical systems: A review of theories and algorithms

Distributed state estimation is of paramount importance in many applications involving the large-scale complex systems over spatially deployed networked sensors. This paper provides an overview for analysis of distributed state estimation algorithms for linear time invariant systems. A number of previous works are reviewed and a clear classification of the main approaches in this field are presented, i.e., Kalman-filter-type methods and Luenberger-observer-type methods. The design and the stabil...     

Adaptive Gaussian sum squared-root cubature Kalman filter with split-merge scheme for state estimation

The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cubature Kalman filter（AGSSCKF） with a split-merge scheme is proposed. It is developed based on the squared-root extension of newly introduced cubature Kalman filter（SCKF） and is built within a Gaussian-sum framework. Based on the condition that the probability density functions of process noises and initial state are denoted by a Gaussian sum using optimization method, a bank of SCKF are used as the sub-filters to estimate state of system with the corresponding weights respectively, which is adaptively updated. The new algorithm consists of an adaptive splitting and merging procedure according to a proposed split-decision model based on the nonlinearity degree of measurement. The results of two simulation scenarios（one-dimensional state estimation and bearings-only tracking） show that the proposed filter demonstrates comparable performance to the particle filter with significantly reduced computational cost.