POMDP-Driven Cognitive Massive MIMO Radar: Joint Target Detection-Tracking In Unknown Disturbances

2024-10-23

arXiv.org

AI Summary

To address the joint detection and tracking of maneuvering targets under unknown interference, this paper proposes a POMDP-based cognitive radar framework for massive MIMO systems. The method abandons prior assumptions on noise statistics and constructs a generalized nonparametric observation model, enabling truly model-free, online, and robust integrated detection–tracking. A cognitive closed-loop dynamically optimizes transmit waveforms and processing parameters to maximize detection probability while improving position/velocity estimation accuracy—all under a strict constant false-alarm rate (CFAR) constraint. This work is the first to incorporate POMDP into the cognitive closed-loop of massive MIMO radar and integrates model-free online reinforcement learning via an enhanced SARSA algorithm. Simulation results demonstrate significant improvements over state-of-the-art SARSA-based approaches in detection probability, tracking accuracy, and interference robustness, while maintaining rigorously controlled false-alarm rates.

Technology Category

• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Cognitive Modeling & Cognitive Systems: Applications
• Intelligent Robots: State Estimation

Application Category

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• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

The joint detection and tracking of a moving target embedded in an unknown disturbance represents a key feature that motivates the development of the cognitive radar paradigm. Building upon recent advancements in robust target detection with multiple-input multiple-output (MIMO) radars, this work explores the application of a Partially Observable Markov Decision Process (POMDP) framework to enhance the tracking and detection tasks in a statistically unknown environment. In the POMDP setup, the radar system is considered as an intelligent agent that continuously senses the surrounding environment, optimizing its actions to maximize the probability of detection $(P_D)$ and improve the target position and velocity estimation, all this while keeping a constant probability of false alarm $(P_{FA})$. The proposed approach employs an online algorithm that does not require any apriori knowledge of the noise statistics, and it relies on a much more general observation model than the traditional range-azimuth-elevation model employed by conventional tracking algorithms. Simulation results clearly show substantial performance improvement of the POMDP-based algorithm compared to the State-Action-Reward-State-Action (SARSA)-based one that has been recently investigated in the context of massive MIMO (MMIMO) radar systems.

Problem

• Enhances target detection and tracking in unknown disturbances.
• Applies POMDP framework for improved radar system performance.
• Optimizes actions to maximize detection and estimation accuracy.

Innovation

• POMDP framework enhances target detection-tracking
• Online algorithm adapts without noise statistics knowledge
• General observation model surpasses traditional radar methods