Joint Multi-Target Detection-Tracking in Cognitive Massive MIMO Radar via POMCP
This work addresses adaptive resource allocation for multi-target radar systems, offering incremental improvements in detection and tracking efficiency.
The paper tackled joint detection and tracking of multiple targets in massive MIMO radar by extending a single-target POMCP algorithm to multi-target scenarios with independent trees and adaptive power allocation, resulting in improved detection probability for low-SNR targets and more accurate tracking compared to uniform or orthogonal waveform methods.
This correspondence presents a power-aware cognitive radar framework for joint detection and tracking of multiple targets in a massive multiple-input multiple-output (MIMO) radar environment. Building on a previous single-target algorithm based on Partially Observable Monte Carlo Planning (POMCP), we extend it to the multi-target case by assigning each target an independent POMCP tree, enabling scalable and efficient planning. Departing from uniform power allocation, which is often suboptimal with varying signal-to-noise ratios (SNRs), our approach predicts each target's future angular position and expected received power based on its expected range. These predictions guide adaptive waveform design via a constrained optimization problem that allocates transmit energy to enhance the detectability of weaker or distant targets, while ensuring sufficient power for high-SNR targets. Simulations involving multiple targets with different SNRs confirm the effectiveness of our method. The proposed framework for the cognitive radar improves detection probability for low-SNR targets and achieves more accurate tracking compared to approaches using uniform or orthogonal waveforms. These results demonstrate the potential of the POMCP-based framework for adaptive, efficient multi-target radar systems.