Distributed fusion estimation in networked systems with uncertain observations and Markovian random delays

Kalman Filtering for Discrete Stochastic Systems with Multiplicative Noises and Random Two-Step Sensor Delays

“…Based on the innovation analysis approach and the recursive projection formula, for both the multiplicative noises and the random two-step sensor delays, a new optimal Kalman filtering has been proposed for the addressed linear stochastic system. Further research topics include the extension of the developed optimal filtering strategy to the prevalent eventtriggered case [35], more networked induced phenomena as in [36], and the random delays modeled by the Markov chain [37]. Moreover, it would be interesting and important to deal with the stability analysis issue for the proposed filtering algorithm.…”

Estimation, filtering and fusion for networked systems with network-induced phenomena: New progress and prospects

“…A globally optimal distributed Kalman filtering fusion method has been proposed in [166] for a class of timevarying systems, where the developed fusion algorithm has the advantage to decrease the computational burden and address the case when the filtering error covariance is singular. For the case that the state-space model of the signal is unavailable, both distributed and centralized fusion schemes have been developed in [167] to deal with the phenomena of the multi-sensor random measurement delays which are modeled by the homogeneous Markov chains and, subsequently, the extended result has been given in [168] to handle the missing measurements and random measurement delays with individual delay rate in a unified framework. Moreover, the distributed Kalman filtering fusion problems have been studied in [38], [169] for networked systems with missing measurements, random transmission delays and packet dropouts, new distributed fusion Kalman filters have been designed based on the innovation analysis method and matrixweighted fusion mechanism.…”

Distributed weighted fusion estimation for uncertain networked systems with transmission time-delay and cross-correlated noises

“…The information fusion steadystate Kalman filtering approach [10] involves different local dynamic models and correlated noises. Song et al presents Kalman filtering fusion [11] with 25 feedback and cross-correlated sensor noises for distributed recursive state estimators. The cross-correlation between the measurement noise and process noise are discussed in [12,13,14].…”

“…The augmented state approach [18,19,20] applies the partial differential equation (PDE) and boundary condition equation, and the polynomial approach [21,22] is utilized to solve the multiple time-delay systems. In order to reduce the communication burden, the measurement transformation approach [17, 23,24,25] uses the reorganized 40 measurement sequence, and the delayed system is transformed into the form of the equivalent delay-free counterpart. However, the aforementioned literature is confined to the state augmentation method to deal with transmission time-delay, and the approach increases the processing capability of estimator over distributed networked systems with cross-correlated noises and transmis-45 sion time-delay.…”