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

2025-07-23

AI Summary

To address the performance degradation of joint multi-target detection and tracking in cognitive massive MIMO radar under dynamic SNR conditions, this paper proposes an adaptive waveform design method based on POMDP modeling and online planning via Partially Observable Monte Carlo Planning (POMCP). The key contributions are threefold: (i) a parallel multi-target POMCP extension framework, wherein independent planning trees are maintained for each target; (ii) a composite reward function integrating angular prediction accuracy and received power estimation error; and (iii) joint optimization subject to waveform orthogonality constraints and transmit power allocation. Simulation results demonstrate that, compared to uniform and orthogonal waveform baselines, the proposed method significantly improves weak-target detection probability under low-SNR conditions (+28.6%) and enhances multi-target tracking accuracy (34.1% reduction in position RMSE), thereby validating its effectiveness and scalability in cognitive sensing applications.

Technology Category

• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Search and Optimization: Mixed Discrete/Continuous Search
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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 estimated range and radar cross-section (RCS). 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. The reward function in the underlying partially observable Markov decision process (POMDP) is also modified to prioritize accurate spatial and power estimation. 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.

Problem

• Extends single-target POMCP to multi-target detection-tracking in MIMO radar
• Optimizes power allocation for targets with varying SNRs via adaptive waveforms
• Improves detection of low-SNR targets and tracking accuracy versus uniform methods

Innovation

• Extends POMCP to multi-target with independent trees
• Predicts target position and power for adaptive waveforms
• Optimizes power allocation to enhance weak targets