Multi‐sensor‐based H _∞ estimation in heterogeneous sensor networks with stochastic competitive transmission and random sensor failures

Abstract

This study investigates the multi-sensor-based centralised estimation problem in heterogeneous sensor networks with a common communication channel. Owing to the heterogeneity of the distributed sensors, it is usually impossible to package the measurements into one packet and transmit them to the fusion centre (FC) together, which implies that the measurements should be forwarded to the FC asynchronously. In view of this, a novel stochastic competitive transmission strategy is proposed to stagger the sensors' transmissions. By using the asynchronous sampled information from the sensors, an H ∞ filter is designed for the FC to periodically generate estimates. The filter parameters are determined by solving a linear-matrix inequality. An illustrative example is provided to demonstrate the effectiveness of the proposed theoretical results.

Distributed filtering of networked dynamic systems with non-Gaussian noises over sensor networks: A survey

“…For instance, the sequential design approach coupled with the minimum principle of Pontryagin and the Lagrange multiplier method has been employed in [106] to deal with the heterogeneity of sensors to realize the unbiasedness and optimality of distributed consensus filtering. For the asynchronization induced by heterogeneous sensors, a stochastic competitive transmission strategy has been developed in [66] to govern sensors' transmissions and then an H ∞ filter has been designed to periodically generate estimates. We should point out how to handle the heterogeneity of sensors to facilitate the filter design still remain largely unexplored.…”

Fusion Estimation for Discrete Time-Varying Systems With Bounded Nonlinearities

“…Remark 4. On one hand, the convex optimization problems (25), (27), (29) and (31) are established in terms of linear matrix inequalities, and thus they can be directly solved by the function ''mincx'' of MATLAB LMI Toolbox [37]. On the other hand, the dimensions of the matrix inequalities in (29) and (31) are dependent on the number of sensors (i.e., L).…”

Cooperative target estimation of strap-down missiles considering random measurement delay

“…To effectively manage modeling mistakes and noise uncertainty, the H ∞ estimation method is derived. Literature studies 15,16 studied the H 2 and H ∞ filtering problems for multi-rate linear time-invariant system; By fusing the measured values during an estimation update period, literature 17 found the necessary criteria for mean squared stability and H ∞ stability and provides an estimate of H ∞ . The issue of finite horizon H ∞ estimation for time-varying multi-rate systems was researched in the literature studies 18,19 under stochastic communication protocols.…”