An approach to distributed asynchronous multi-sensor fusion utilising data compensation algorithm

Multi-sensor networks often encounter challenges such as inconsistent sampling times among local sensors and data loss during transmission. To address these issues, this paper employs a data loss compensation strategy to reconstruct missing data information. It designs the state estimation of local sensors utilising iterative state equations, leveraging multistep prediction techniques to estimate sensor states at unsampled points, thereby transforming the asynchronous sensor network system into a synchronous one. Furthermore, the projection theorem is applied to determine the fusion weights of local sensors, grounded on the principle of square-averaging significance. Ultimately, data information pertaining to the same target is fused through arithmetic averaging, guided by distance correlation. Simulation outcomes demonstrate that the proposed algorithm balances estimation accuracy with communication overhead, achieved by designing an optimal number of communication iterations.