Hybrid Cognition for Target Tracking in Cognitive Radar Networks

TL;DR

This paper presents a hybrid cognition approach for cognitive radar networks that uses online learning and feedback to optimize channel selection for target tracking and spectrum sharing, reducing interference and learning time.

Contribution

It introduces a hybrid cognition model where both radar nodes and a central coordinator learn and adapt online for optimal spectrum sharing and target tracking.

Findings

Hybrid cognition improves learning efficiency in spectrum sharing.

Limited central coordination reduces channel collisions.

The approach enhances radar tracking accuracy while minimizing interference.

Abstract

This work investigates online learning techniques for a cognitive radar network utilizing feedback from a central coordinator. The available spectrum is divided into channels, and each radar node must transmit in one channel per time step. The network attempts to optimize radar tracking accuracy by learning the optimal channel selection for spectrum sharing and radar performance. We define optimal selection for such a network in relation to the radar observation quality obtainable in a given channel. This is a difficult problem since the network must seek the optimal assignment from nodes to channels, rather than just seek the best overall channel. Since the presence of primary users appears as interference, the approach also improves spectrum sharing performance. In other words, maximizing radar performance also minimizes interference to primary users. Each node is able to learn the quality of several available channels through repeated sensing. We define hybrid cognition as the condition where both the independent radar nodes as well as the central coordinator are modeled as cognitive agents, with restrictions on the amount of information that can be exchanged between the radars and the coordinator. Importantly, each part of the network acts as an online learner, observing the environment to inform future actions. We show that in interference-limited spectrum, where the signal-to-interference-plus-noise ratio varies by channel and over time for a target with fixed radar cross section, a cognitive radar network is able to use information from the central coordinator in order to reduce the amount of time necessary to learn the optimal channel selection. We also show that even limited use of a central coordinator can eliminate collisions, which occur when two nodes select the same channel.