Practical Aspects of Cognitive Radar

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

doi:10.1109/radarconf2043947.2020.9266646

Cited by 11 publications

(4 citation statements)

References 34 publications

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“…These three factors are necessary elements for future CR models that require a hierarchy of PAC loops. This paper extends the discussion in [20], where the factors above were introduced, by providing further analysis, outlining potential hardware requirements and limitations, discussing solutions to challenges that occur when the PAC changes too fast or too slow relative to the environment, presenting a scalable model for the practical implementation of CR, and highlighting how metrics are impacted by this model. This model is intentionally generic to address several radar applications; however, our focus here is the spectrum sharing application in order to demonstrate implementation for autonomous, real-time radar (described in Section II).…”

Section: • Autonomous Regulation Of Cognitionmentioning

confidence: 83%

Closing the Loop on Cognitive Radar for Spectrum Sharing

Martone

Sherbondy

Kovarskiy

et al. 2021

IEEE Aerosp. Electron. Syst. Mag.

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We investigate three emerging topics essential for the development of cognitive radar (CR) for spectrum sharing: the response time (RT) of the CR, the autonomous regulation of the perception-action cycle (PAC), and the regulation of cognition. The RT measures the latency of all algorithms / hardware and is examined with respect to enabling capabilities of software-defined systems for rapid flexibility and responsiveness. The autonomous regulation of the PAC determines "how fast the CR can interact with the environment" as well as "how fast the CR should interact with the environment." The regulation of the PAC is explored with respect to pulse-to-pulse waveform agility to coexist successfully with dynamic radio frequency (RF) emitters in the ambient electromagnetic environment (EME) and the consequence of modifying the waveform within the coherent processing interval (CPI). Finally, the regulation of cognition determines how to select a particular CR technique appropriately for a given dynamic environment. This selection requires a high-level, or meta-decision process to identify the appropriate cognitive radar technique as the EME changes over time and we therefore concentrate discussion on the newly emerging topic for radar called metacognitive radar. The exploration of these three topics include a review of past and current research with discussion of possible future research.

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Section: • Autonomous Regulation Of Cognitionmentioning

confidence: 83%

Closing the Loop on Cognitive Radar for Spectrum Sharing

Martone

Sherbondy

Kovarskiy

et al. 2021

IEEE Aerosp. Electron. Syst. Mag.

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“…CONCLUSION A constrained online learning approach for pulse-agile cognitive radar was presented. This structure can be applied to a wide range of decision-making algorithms 3 , demonstrated by the constrained linear TS and EXP3 algorithms presented in Algorithms 1 and 2. Through simulations in dynamic radarcommunication coexistence settings, the proposed scheme was demonstrated to reduce distortion effects for favorable detection performance when a cost function based on interference avoidance is used.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…1 The total number of unique values the interference vector can take is thus 2 S Definition 1. Define the collision bandwidth as (3) where f start and f end are the lowest and highest frequencies in the shared channel and the sum is taken with precision (f end − f start )/S. BW c thus corresponds to the fraction of the shared channel bandwidth occupied by both the radar's waveform w i and the interference vector s t .…”

Section: Introductionmentioning

confidence: 99%

Constrained Online Learning to Mitigate Distortion Effects in Pulse-Agile Cognitive Radar

Thornton¹,

Buehrer²,

Martone³

2020

Preprint

Self Cite

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Pulse-agile radar systems have demonstrated favorable performance in dynamic electromagnetic scenarios. However, the use of non-identical waveforms within a radar's coherent processing interval may lead to harmful distortion effects when pulse-Doppler processing is used. This paper presents an online learning framework to optimize detection performance while mitigating harmful sidelobe levels. The radar waveform selection process is formulated as a linear contextual bandit problem, within which waveform adaptations which exceed a tolerable level of expected distortion are eliminated. The constrained online learning approach is effective and computationally feasible, evidenced by simulations in a radar-communication coexistence scenario and in the presence of intentional adaptive jamming. This approach is applied to both stochastic and adversarial contextual bandit learning models and the detection performance in dynamic scenarios is evaluated.

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“…The use of software defined radio (SDR) for research and development of radar applications has become attractive inasmuch as the flexibilization of the system increases by the facility in the change of (radio frequency) RF parameters such as frequency and gain [1], [2]. The integration of these two technologies is known as SDRadar and it has the advantage of being able to change the architecture of the radar by means of programming, while keeping the same hardware [3], [4]. SDRadar has the facility of modifying parameters of RF functioning through software and has allowed researchers and scholars to experiment different low-cost radar techniques with the help of free software such as GNU-radio.…”

Section: Introductionmentioning

confidence: 99%

Reconfigurable software-defined radar testbed with built-in validation

2022

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This article presents a radar testbed for speed detection through micro-doppler effect in a controlled environment using software defined radio (SDR) technology. The target moves along a conveyor belt with software-controlled speed. The speed is detected by an SDR radar, and it is possible to compare it to an encoder-based sensor implemented on the testbed. The testbed as well as the SDR radar are reconfigurable and a (continuous wave) CW radar was implemented for the validation of the testbed; however, the testbed is not limited to this implementation. The testbed can be remotely operated because it includes the mechanism to move the target and control its velocity. The article shows the way in which the testbed was designed and implemented, the generation and processing of the radar signal using a limeSDR, and the validation of the radar measurements compared to the encoder-based speed sensor. The maximum speed obtained by the target in the testbed is 15.69cm/s. Results show a difference in the speed measured with the SDRadar of no more than 5% compared to the sensor measurememt. Results obtained allow characterizing the behavior of the SDR platform in the detection of low speeds.

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Practical Aspects of Cognitive Radar

Cited by 11 publications

References 34 publications

Closing the Loop on Cognitive Radar for Spectrum Sharing

Closing the Loop on Cognitive Radar for Spectrum Sharing

Constrained Online Learning to Mitigate Distortion Effects in Pulse-Agile Cognitive Radar

Reconfigurable software-defined radar testbed with built-in validation

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