Joint Multi-Target Detection-Tracking in Cognitive Massive MIMO Radar via POMCP

TL;DR

This paper introduces a cognitive MIMO radar framework that adaptively allocates power to improve multi-target detection and tracking under unknown disturbances using POMCP-based waveform design.

Contribution

It proposes a novel POMCP-driven adaptive waveform strategy for multi-target MIMO radar, enhancing detection and tracking performance over non-adaptive methods.

Findings

Detection probability for low-SNR targets increased from 0.6 to 0.9.

Achieved more accurate tracking of weaker targets.

Outperformed non-adaptive and uniform-power baseline methods.

Abstract

This work presents a cognitive radar (CR) framework to enhance remote sensing performance, specifically focusing on tracking multiple targets under unknown disturbances using massive multiple-input multiple-output (MMIMO) systems. Since uniform power allocation is suboptimal across varying signal-to-noise ratios (SNRs), we propose an adaptive waveform design driven by Partially Observable Monte Carlo Planning (POMCP). By assigning an independent POMCP tree to each target, the system efficiently predicts target states. These predictions inform a constrained optimization problem that actively directs transmit energy toward weaker targets while maintaining sufficient power for stronger ones. Results confirm that the proposed POMCP method improves the detection probability for low-SNR targets from 0.6 to nearly 0.9, and yields more accurate tracking of the weakest target than a non-adaptive orthogonal waveform or a cognitive uniform-power POMCP baseline.