Optimal Unbiased Linear Sensor Fusion over Multiple Lossy Channels with Collective Observability

TL;DR

This paper develops an optimal linear sensor fusion method for remote state estimation over lossy channels, assuming collective observability but no local observability, with a convex optimization solution and closed-form coefficients.

Contribution

It introduces a convex optimization framework and closed-form solutions for optimal sensor fusion in scenarios lacking local observability but with collective observability.

Findings

The proposed method achieves improved estimation accuracy.

The convex optimization approach efficiently computes optimal fusion coefficients.

Simulation results validate the effectiveness of the algorithm.

Abstract

In this paper, we consider optimal linear sensor fusion for obtaining a remote state estimate of a linear process based on the sensor data transmitted over lossy channels. There is no local observability guarantee for any of the sensors. It is assumed that the state of the linear process is collectively observable. We transform the problem of finding the optimal linear sensor fusion coefficients as a convex optimization problem which can be efficiently solved. Moreover, the closed-form expression is also derived for the optimal coefficients. Simulation results are presented to illustrate the performance of the developed algorithm.