Recursive distributed fusion estimation for multi-sensor systems with missing measurements, multiple random transmission delays and packet losses

This paper is concerned with the recursive distributed fusion estimation problems for networked multi -sensor systems with missing measurements, multiple random transmission delays, and packet losses. There exist missing measurements in the observation equations due to unpredictable sensor faults. More-over, there are often random delays and losses during data transmissions from sensors to the fusion center due to limited communication bandwidths of the network. The Kalman-like recursive distributed fusion predictor and filter in the linear unbiased minimum variance (LUMV) sense are, respectively pre-sented based on local estimates, cross-covariance matrices between local estimates, and cross-covariance matrices between the prior fusion estimate and local estimates. The stability and steady-state property of the proposed algorithms are analyzed. Their estimation accuracy is better than that of local estimates and distributed fusion estimates by matrix-weighting local estimates. A simulation example shows the effectiveness of the proposed algorithms.(c) 2022 Elsevier B.V. All rights reserved.