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

arXiv: https://arxiv.org/abs/2410.17967 · HTML: https://mlsanity.com/paper/2410.17967/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 23 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Deep Recurrent Q-Learning for Partially Observable MDPs](https://mlsanity.com/paper/1507.06527/) (2015)
- [Online algorithms for POMDPs with continuous state, action, and observation spaces](https://mlsanity.com/paper/1709.06196/) (2017)
- [Massive MIMO Radar for Target Detection](https://mlsanity.com/paper/1906.06191/) (2019)
- [Voronoi Progressive Widening: Efficient Online Solvers for Continuous State, Action, and Observation POMDPs](https://mlsanity.com/paper/2012.10140/) (2020)
- [Enhancement of a state-of-the-art RL-based detection algorithm for Massive MIMO radars](https://mlsanity.com/paper/2112.02628/) (2021)

## Built on by (most cited first, 1 of 1)

- [Power-Aware Cognitive Radar Multi-target Tracking Under Unknown Disturbances](https://mlsanity.com/paper/2507.17506/) (2025)

## Abstract (arXiv)

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 (PD) and improve the target position and velocity estimation, all this while keeping a constant probability of false alarm (PFA). 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.