Sparse Sensing Architectures with Optimal Precision for Tracking Multi-agent Systems in Sensing-denied Environments

TL;DR

This paper develops a method to optimize sensor precisions and sparsity in multi-agent tracking within sensing-denied environments, balancing measurement accuracy and resource efficiency.

Contribution

It introduces a novel optimization framework using semi-definite programming to determine optimal sensor precisions and promote measurement sparsity for reliable tracking.

Findings

Optimal sensor precisions improve tracking accuracy.

Sparsity in measurements reduces resource usage.

Trade-off identified between sensor precision and sensing frequency.

Abstract

In this paper the tracking problem of multi-agent systems, in a particular scenario where a segment of agents entering a sensing-denied environment or behaving as non-cooperative targets, is considered. The focus is on determining the optimal sensor precisions while simultaneously promoting sparseness in the sensor measurements to guarantee a specified estimation performance. The problem is formulated in the discrete-time centralized Kalman filtering framework. A semi-definite program subject to linear matrix inequalities is solved to minimize the trace of precision matrix which is defined to be the inverse of sensor noise covariance matrix. Simulation results expose a trade-off between sensor precisions and sensing frequency.