Timely Target Tracking in Cognitive Radar Networks

TL;DR

This paper introduces an Age of Information-inspired metric for target update selection in communication-limited cognitive radar networks, improving tracking efficiency when nodes have partial, noisy observations.

Contribution

It proposes a novel track-sensitive metric based on Age of Information for fusion center decision-making in radar networks with limited communication.

Findings

The proposed metric outperforms traditional selection techniques.

It effectively manages partial and noisy target observations.

Simulation results demonstrate improved tracking performance.

Abstract

We consider a scenario where a fusion center must decide which updates to receive during each update period in a communication-limited cognitive radar network. When each radar node in the network only is able to obtain noisy state measurements for a subset of the targets, the fusion center may not receive updates on every target during each update period. The solution for the selection problem at the fusion center is not well suited for sequential learning frameworks. We derive an Age of Information-inspired track sensitive metric to inform node selection in such a network and compare it against less-informed techniques.