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ODEFTC: Optimal Distributed Estimation based on Fixed-Time Consensus
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DOI：10.1016/j.inffus.2024.102783.png)
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Abstract
En 中文
Distributed state estimation has been a significant research topic in recent years due to its applications for multi-robot and large-scale systems. Several approaches have been proposed in the context of continuous-time systems with stochastic noise, with limitations regarding observability, assumptions on the noise bounds, or requirements to pre-compute auxiliary global information offline. Moreover, many of these approaches are suboptimal with respect to a centralized implementation, and optimal proposals only apply to time-invariant systems. The present work proposes the ODEFTC algorithm for distributed state estimation based on fixed- time consensus. The proposal computes state estimates and corresponding covariance matrices online, making it suitable for time-variant systems. We verify the stability of the proposal through formal analysis, and we show that the optimal centralized solution, given by the Kalman-Bucy filter, can be recovered asymptotically. Additionally, we provide numerical results and an in-depth statistical and numerical discussion to show the advantages of our proposal against other approaches in the literature.
Keywords:
Distributed state estimation
Sensor networks
Multi-agent systems
Fixed-time consensus
Continuous-time stochastic systems