Comparing stochastic and Markov decision process approaches for predicting radio frequency interference

Kovarskiy, Jacob A.; Kozy, Mark; Thornton, Charles E.; Martone, Anthony F.; Narayanan, Ram M.; Buehrer, R. Michael; Sherbondy, Kelly D.

doi:10.1117/12.2519675

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…intelligent jammers) [35]. In [36] the SPA and SLA CR techniques were compared and found to yield similar performance in many stochastic scenarios.…”

Section: Cognitive Radar Techniques For Spectrum Sharingmentioning

confidence: 99%

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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“…intelligent jammers) [35]. In [36] the SPA and SLA CR techniques were compared and found to yield similar performance in many stochastic scenarios.…”

Section: Cognitive Radar Techniques For Spectrum Sharingmentioning

confidence: 99%

Closing the Loop on Cognitive Radar for Spectrum Sharing

Martone

Sherbondy

Kovarskiy

et al. 2021

IEEE Aerosp. Electron. Syst. Mag.

Self Cite

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“…These methods require significant memory resources and time to converge to an optimal policy. Previous work in [43] compares a stochastic method to an MDP model and found that the stochastic approach has slightly better prediction accuracy for random RFI patterns with less computation during training. Once again, [43] does not evaluate the effects of spectrum sharing on radar processing.…”

Section: B Spectral Predictionmentioning

confidence: 99%

“…Previous work in [43] compares a stochastic method to an MDP model and found that the stochastic approach has slightly better prediction accuracy for random RFI patterns with less computation during training. Once again, [43] does not evaluate the effects of spectrum sharing on radar processing. In addition to computational benefits, the model-based approach could provide metadata describing the RF environment to other cognitive radar subsystems [44].…”

Section: B Spectral Predictionmentioning

confidence: 99%

Evaluation of Real-Time Predictive Spectrum Sharing for Cognitive Radar

Kovarskiy

Kirk

Martone

et al. 2021

IEEE Trans. Aerosp. Electron. Syst.

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The growing demand for radio frequency (RF) spectrum access poses new challenges for nextgeneration radar systems. To operate in a crowded electromagnetic environment, radars must coexist with other RF emitters while maintaining system performance. This work evaluates the performance of a spectrum sharing cognitive radar system which predicts and avoids RF interference in real-time. The system applies a cognitive perceptionaction cycle which senses RF interference (RFI), learns RFI behavior over time, and adapts the radar's frequency band of operation. Through cognition, the system learns a stochastic model describing RF activity. This model allows the cognitive radar to predict RF activity in real-time and share the spectrum with emitters such as communication systems. A set of synthetic and measured interference signals are used to evaluate the performance of this predictive spectrum sharing scheme. This work assesses the impact of RF interference on the cognitive radar's range profile with respect to variation in RF environment. The system demonstrates accurate avoidance of deterministic RFI with a degradation in spectrum sharing efficiency as variability over time increases.

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“…Activity prediction can deduce future environmental states based on observations to improve the accuracy of environmental knowledge, which has been extensively studied in the communication field [13][14][15], and a few research results have been extended to radar applications [8,9,[16][17][18][19][20][21][22]. These prediction methods can be divided into model-based and model-independent methods, and the main techniques involve Markov process-based prediction [8][9][10]16,18], stochastic process-based prediction [19,20] and machine learningbased prediction [21,22]. Stinco et al [18] presented a single-channel hidden Markov model (HMM)-based prediction method.…”

Section: Introductionmentioning

confidence: 99%

Online Sequential Extreme Learning Machine-Based Active Interference Activity Prediction for Cognitive Radar

et al. 2022

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For anti-active-interference-oriented cognitive radar systems, the mismatch between the acquired and actual interference information may result in serious degradation of cognitive anti-active-interference performance. To yield more effective knowledge of the electromagnetic environment and eliminate the mismatch effect, the electromagnetic activity prediction technique, which deduces future electromagnetic behaviors based on current observations, has received increasing attention. However, high computational complexities limit the application of conventional electromagnetic activity prediction methods in dynamic active interference prediction with high real-time requirements. In this paper, the online sequential extreme learning machine (OS-ELM)-based method, which is dedicated to high-efficiency active interference activity prediction, is proposed. The advancement includes two aspects. First, benefiting from the single-hidden-layer network structure and recursive-formula-based output weight updating, the proposed OS-ELM-based frequency prediction (OS-ELM-FP) and OS-ELM-based angle prediction (OS-ELM-AP) models can predict the interference state and update the prediction model parameters with much higher computational efficiency. Second, the better generalization performance enables the proposed method to achieve smaller interference activity prediction errors compared with conventional methods. Numerical examples and prediction results based on measured jamming data demonstrate the advantages of the proposed method.

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Comparing stochastic and Markov decision process approaches for predicting radio frequency interference

Cited by 6 publications

References 14 publications

Closing the Loop on Cognitive Radar for Spectrum Sharing

Closing the Loop on Cognitive Radar for Spectrum Sharing

Evaluation of Real-Time Predictive Spectrum Sharing for Cognitive Radar

Online Sequential Extreme Learning Machine-Based Active Interference Activity Prediction for Cognitive Radar

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