Fusion Predictors for Multisensor Stochastic Uncertain Systems With Missing Measurements and Unknown Measurement Disturbances

Abstract

This paper addresses the information fusion state estimation problem for multisensor stochastic uncertain systems with missing measurements and unknown measurement disturbances. The missing measurements of sensors are described by Bernoulli distributed random variables. Measurements of sensors are subject to external disturbances whose any prior information is unknown. Stochastic parameter uncertainties of systems are depicted by multiplicative noises. For such complex systems with multiple sensors, the Kalman-like centralized fusion and distributed fusion state one-step predictors (i.e., prior filters) independent of unknown measurement disturbances are designed based on the linear unbiased minimum variance criterion, respectively. Estimation error cross-covariance matrices between any two local predictors are derived. Their steady-state properties are analyzed. The sufficient conditions for the existence of the steady-state predictors are given. Two simulation examples show the effectiveness of the proposed algorithms.Index Terms-Multi-sensor, missing measurement, unknown disturbance, multiplicative noise, fusion predictor, linear unbiased minimum variance.

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

“…On the other hand, according to the matrix-weighted fusion mechanism, several distributed fusion algorithms have been developed in order to improve the fault-tolerance ability [160]- [163]. To be more specific, the Kalman-like distributed fusion filters (one-step predictors) have been constructed in [160] for linear multi-sensor time-varying stochastic system in the simultaneous presence of parameter uncertainties, missing measurements and unknown measurement disturbances, and the optimal filter gains have been obtained based on the linear unbiased minimum variance criterion.…”

“…To be more specific, the Kalman-like distributed fusion filters (one-step predictors) have been constructed in [160] for linear multi-sensor time-varying stochastic system in the simultaneous presence of parameter uncertainties, missing measurements and unknown measurement disturbances, and the optimal filter gains have been obtained based on the linear unbiased minimum variance criterion. In [161], the distributed fusion estimation algorithm has been given for linear discrete time-varying stochastic systems with multi-sensor missing observations, where the case of the finite-step observations missing has been discussed.…”

Fusion Estimation from Multisensor Observations with Multiplicative Noises and Correlated Random Delays in Transmission

“…Multiplicative noise uncertainties and missing measurements are some of the random phenomena that usually arise in the sensor measured outputs and motivate the design of new estimation algorithms (see e.g., [7][8][9][10][11], and references therein).…”

“…As in [25], correlated random delays in the transmission are assumed to exist, with different delay rates at each sensor; however, the proposed observation model is more general than that considered in [25] since, besides the random delays in the transmission, multiplicative noises and missing phenomena in the measured outputs are considered; also cross-correlation between the different sensor additive noises is taken into account. Unlike [7][8][9][10][11] where multiplicative noise uncertainties and/or missing measurements are considered in the sensor measured outputs, in this paper random delays in the transmission are also assumed to exist. Hence, a unified framework is provided for dealing simultaneously with missing measurements and uncertainties caused by multiplicative noises, along with random delays in the transmission and, hence, the proposed fusion estimators have wide applicability.…”

Distributed Fusion Filter for Networked Multi-sensor Systems with Unknown Measurement Interferences and Packet Dropouts

“…Ref. [14] gives a fusion predictor for multi-sensor systems with missing measurements and unknown measurement interferences, where the computation of the state second-order moment is required.…”