Multi-sensor State Estimation over Lossy Channels using Coded Measurements
For control engineers designing networked estimation systems, this work offers a theoretically optimal coding method to balance communication load and estimator stability under lossy channels.
The paper proposes a measurement coding scheme for networked state estimation in large linear systems with lossy channels, achieving optimal communication-stability trade-off within linear causal coders. The scheme provides necessary and sufficient stability conditions with a trivial gap and quantifies performance advantages.
This paper focuses on a networked state estimation problem for a spatially large linear system with a distributed array of sensors, each of which offers partial state measurements, and the transmission is lossy. We propose a measurement coding scheme with two goals. Firstly, it permits adjusting the communication requirements by controlling the dimension of the vector transmitted by each sensor to the central estimator. Secondly, for a given communication requirement, the scheme is optimal, within the family of linear causal coders, in the sense that the weakest channel condition is required to guarantee the stability of the estimator. For this coding scheme, we derive the minimum mean-square error (MMSE) state estimator, and state a necessary and sufficient condition with a trivial gap, for its stability. We also derive a sufficient but easily verifiable stability condition, and quantify the advantage offered by the proposed coding scheme. Finally, simulations results are presented to confirm our claims.