Adversarial Multi-Player Bandits for Cognitive Radar Networks

Howard, William W.; Martone, Anthony F.; Buehrer, R. Michael

doi:10.31219/osf.io/7e83v

Cited by 1 publication

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This paper extends preliminary versions which were published previously [1], [2]. We previously investigated the available MMAB algorithms, and in [1] studied reward structures which differ between radar nodes.…”

Section: B Contributionsmentioning

confidence: 70%

“…We previously investigated the available MMAB algorithms, and in [1] studied reward structures which differ between radar nodes. In [2], we analyzed algorithms tailored towards adversarial environments, where the environment has knowledge of the algorithm being used by the radar network, and can pre-select a reward sequence in an attempt to worsen the performance of the network. Rather than focus on these different environment classes, this current work is interested in developing the supporting models for a MMAB algorithm used for waveform selection in cognitive radar networks.…”

Section: B Contributionsmentioning

confidence: 99%

“…Varieties of the proposed sequential decision-making problem has been studied in cognitive radio, as well as in cognitive radar [2], [35]. The approach in this work employs a multiplayer multi-arm bandit framework to allow nodes to develop a model of their environment, without relying on a central decision-maker [1].…”

Section: E Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

Distributed Online Learning for Coexistence in Cognitive Radar Networks

Howard¹,

Martone²,

Buehrer³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

This work addresses the coexistence problem for radar networks. Specifically, we model a network of cooperative, independent, and non-communicating radar nodes which must share resources within the network as well as with non-cooperative nearby emitters. We approach this problem using online Machine Learning (ML) techniques. Online learning approaches are specifically preferred due to the fact that each radar node has no prior knowledge of the environment nor of the positions of the other radar nodes, and due to the sequential nature of the problem. For this task we specifically select the multi-player multi-armed bandit (MMAB) model, which poses the problem as a sequential game, where each radar node in a network makes independent selections of center frequency and waveform with the same goal of improving tracking performance for the network as a whole. For accurate tracking, each radar node communicates observations to a fusion center on set intervals. The fusion center has knowledge of the radar node placement, but cannot communicate to the individual nodes fast enough for waveform control. Every radar node in the network must learn the behavior of the environment, which includes rewards, interferer behavior, and target behavior. Each independent and identical node must choose one of many waveforms to transmit in each Pulse Repetition Interval (PRI) while avoiding \emph{collisions} with other nodes and interference from the environment. The goal for the network as a whole is to minimize target tracking error, which relies on obtaining high SINR in each time step. Our contributions include a mathematical description of the MMAB framework adapted to the radar network scenario. We conclude with a simulation study of several different network configurations. Experimental results show that iterative, online learning using MMAB outperforms the more traditional sense-and-avoid (SAA) and fixed-allocation approaches.

show abstract

Section: B Contributionsmentioning

confidence: 70%

Section: B Contributionsmentioning

confidence: 99%