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

TL;DR

This paper introduces a POMDP-based cognitive MIMO radar system that adaptively detects and tracks targets in unknown disturbance environments, significantly improving performance without prior noise knowledge.

Contribution

It develops a novel POMDP framework for MIMO radar that enhances target detection and tracking in unknown environments, surpassing previous SARSA-based methods.

Findings

Substantial performance improvement over SARSA-based algorithms

Effective target detection and tracking without prior noise statistics

Enhanced estimation accuracy in complex environments

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.