Closing the Loop on Cognitive Radar for Spectrum Sharing

Martone, Anthony F.; Sherbondy, Kelly D.; Kovarskiy, Jacob A.; Kirk, Benjamin H.; Narayanan, Ram M.; Thornton, Charles E.; Buehrer, R. Michael; Owen, Jonathan W.; Ravenscroft, Brandon; Blunt, Shannon D.; Egbert, Austin; Goad, Adam; Baylis, Charles

doi:10.1109/maes.2021.3072698

Cited by 37 publications

(8 citation statements)

References 56 publications

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“…Cognitive radar systems have been proposed for noncooperative coexistence many times [17], [18], [19]. Specifically of interest in this work are cognitive radar networks, which can combine observations from individual radars in an intelligent manner to benefit the network [4], [20].…”

Section: A Cognitive Radar Networkmentioning

confidence: 99%

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Distributed Online Learning for Coexistence in Cognitive Radar Networks

Howard¹,

Martone²,

Buehrer³

2022

Preprint

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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.

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Section: A Cognitive Radar Networkmentioning

confidence: 99%

“…In the context of radar, cognitive systems have been described using the PAC [18], [21], [22], a closed-loop model which implements cognition as defined above. It has been stressed that a major property of this problem is the interaction between the cognitive system and its environment [19].…”

Section: A Cognitive Radar Networkmentioning

confidence: 99%

Distributed Online Learning for Coexistence in Cognitive Radar Networks

Howard¹,

Martone²,

Buehrer³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Section: A Cognitive Radar Networkmentioning

confidence: 99%

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 noncooperative 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 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 senseand-avoid (SAA) and fixed-allocation approaches.

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“…This is the question which we pursue here. We acknowledge that a practical characterization of online learning performance for all waveform selection tasks is beyond the scope of a single investigation, but present insight towards the general question by examining compelling performance measures for two broad applications of practical interest, namely radar dynamic spectrum access [8], [35], [36] and multiple target tracking [2], [37], [38]. We apply tools from decision theory and information theory to compare the performance of an online learning-based waveform selection algorithm with fixed rule-based strategies.…”

Section: Introductionmentioning

confidence: 99%

Online Meta-Learning for Scene-Diverse Waveform-Agile Radar Target Tracking

Thornton

Buehrer

Martone

2022

2022 IEEE Radar Conference (RadarConf22)

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This paper attempts to characterize the kinds of physical scenarios in which an online learning-based cognitive radar is expected to reliably outperform a fixed rule-based waveform selection strategy, as well as the converse. We seek general insights through an examination of two decision-making scenarios, namely dynamic spectrum access and multiple-target tracking. The radar scene is characterized by inducing a statespace model and examining the structure of its underlying Markov state transition matrix, in terms of entropy rate and diagonality. It is found that entropy rate is a strong predictor of online learning-based waveform selection, while diagonality is a better predictor of fixed rule-based waveform selection. We show that these measures can be used to predict first and second-order stochastic dominance relationships, which can allow system designers to make use of simple decision rules instead of more cumbersome learning approaches under certain conditions. We validate our findings through numerical results for each application and provide guidelines for future implementations.

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Closing the Loop on Cognitive Radar for Spectrum Sharing

Cited by 37 publications

References 56 publications

Distributed Online Learning for Coexistence in Cognitive Radar Networks

Distributed Online Learning for Coexistence in Cognitive Radar Networks

Distributed Online Learning for Coexistence in Cognitive Radar Networks

Online Meta-Learning for Scene-Diverse Waveform-Agile Radar Target Tracking

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