Sensor Selection for Remote State Estimation with QoS Requirement Constraints

TL;DR

This paper addresses the challenge of selecting sensors for remote state estimation under QoS constraints, proposing a relaxation and heuristic algorithm to optimize sensor transmission for improved estimation accuracy.

Contribution

It introduces a novel approach combining relaxation and successive convex approximation to solve the sensor selection problem with QoS constraints.

Findings

The proposed heuristic achieves near-optimal estimation performance.

Simulation results validate the effectiveness of the algorithm.

The method efficiently handles non-convex optimization in sensor selection.

Abstract

In this paper, we study the sensor selection problem for remote state estimation under the Quality-of-Service (QoS) requirement constraints. Multiple sensors are employed to observe a linear time-invariant system, and their measurements should be transmitted to a remote estimator for state estimation. However, due to the limited communication resources and the QoS requirement constraints, only some of the sensors can be allowed to transmit their measurements. To estimate the system state as accurately as possible, it is essential to select sensors for transmission appropriately. We formulate the sensor selection problem as a non-convex optimization problem. It is difficult to solve such a problem and even to find a feasible solution. To obtain a solution which can achieve good estimation performance, we first reformulate and relax the formulated problem. Then, we propose an algorithm based on successive convex approximation (SCA) to solve the relaxed problem. By utilizing the solution of the relaxed problem, we propose a heuristic sensor selection algorithm which can provide a good suboptimal solution. Simulation results are presented to show the effectiveness of the proposed heuristic.