Multi-Target Radar Search and Track Using Sequence-Capable Deep Reinforcement Learning

TL;DR

This paper demonstrates how sequence-capable deep reinforcement learning architectures can enhance radar systems' ability to efficiently search and track multiple targets in complex environments.

Contribution

It introduces a novel application of sequence-capable deep RL architectures for multi-target radar search and tracking, with comparative analysis of neural network designs and pre-training methods.

Findings

Self-attention architecture outperformed others in tracking scenarios

Pre-training techniques improved feature extraction quality

Reinforcement learning optimized sensor management for complex environments

Abstract

The research addresses sensor task management for radar systems, focusing on efficiently searching and tracking multiple targets using reinforcement learning. The approach develops a 3D simulation environment with an active electronically scanned array radar, using a multi-target tracking algorithm to improve observation data quality. Three neural network architectures were compared including an approach using fated recurrent units with multi-headed self-attention. Two pre-training techniques were applied: behavior cloning to approximate a random search strategy and an auto-encoder to pre-train the feature extractor. Experimental results revealed that search performance was relatively consistent across most methods. The real challenge emerged in simultaneously searching and tracking targets. The multi-headed self-attention architecture demonstrated the most promising results, highlighting the potential of sequence-capable architectures in handling dynamic tracking scenarios. The key contribution lies in demonstrating how reinforcement learning can optimize sensor management, potentially improving radar systems' ability to identify and track multiple targets in complex environments.