A Stochastic Model for Prediction and Avoidance of RF Interference to Cognitive Radars

Kovarskiy, Jacob A.; Narayanan, Ram M.; Martone, Anthony F.; Sherbondy, Kelly D.

doi:10.1109/radar.2019.8835523

Cited by 8 publications

(5 citation statements)

References 13 publications

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“…Prediction mitigates these errors at the cost of computational resources. This work builds upon a stochastic model-based prediction approach from [32], [33].…”

Section: B Spectral Predictionmentioning

confidence: 99%

“…2. Previous work in [32], presents results from this approach with a fixed threshold for both states regardless of the RF environment. Compared to this method, the addition of threshold optimization and unique thresholds for each state seeks to improve performance.…”

Section: Prediction and Avoidancementioning

confidence: 99%

“…In this implementation, the grid search computes ρ by exhaustively testing 100 possible threshold values for θ B and θ I between 0.05 and 0.99 to satisfy Equation (13). Based on previous results in [32], the prediction threshold directly controls a tradeoff between collisions and missed opportunities. Since this trade-off is based on a rough performance heuristic, the search size has only minor impacts on performance.…”

Section: Prediction Threshold Tuningmentioning

confidence: 99%

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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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“…Prediction mitigates these errors at the cost of computational resources. This work builds upon a stochastic model-based prediction approach from [32], [33].…”

Section: B Spectral Predictionmentioning

confidence: 99%

Section: Prediction and Avoidancementioning

confidence: 99%

Section: Prediction Threshold Tuningmentioning

confidence: 99%

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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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“…Figure 15 LTE uplink activities at 1750 MHz [7] Depending on the desired collision and missed opportunity trade-off, the appropriate parameter and function choice could achieve attributes as examples present in Figures 16 and 17 show. In deterministic zero variance scenarios, the reactive mode cognitive radar collides during state transitions, while this predictive scheme can perfectly avoid RFI.…”

Section: Figurementioning

confidence: 99%

“…Figure 16 Average collision rate for the 1750 MHz band [7] Figure 17 Average missed opportunity rate for the 1750 MHz band [7] Roland Oechslin and Peter Wellis (Science and Technology, Switzerland), Uwe Aulenbacher, Sebastian Wieland and Sebastian Hinrichse (Ingenieurbüro für Sensorik und Signalverarbeitung, Germany): Cognitive Radar Performance Analysis with Different Types of Targets. [9] This paper presents a research in which detection and tracking experiments with the cognitive adaptation of waveform and processing parameters have been performed.…”

Section: Figurementioning

confidence: 99%

Advanced Issues of the Radar Conference in Boston, 2019

Balajti

2020

Hadmérnök

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The modernisation of the Hungarian Army and the success of the Zrínyi 2026 Programme basically depend on the understanding and professional lifecycle-support of the latest technologies. Consequently, it is a priority to collect, evaluate and transfer advanced research findings and collected expertise on the concepts related to sensors, including Radars. The software modules define the quality, while the flexibility of the interfaces determine the efficiency of the signal and data processing of the information of different sensor types. The software-based solutions play a key role in the artificial intelligence supported cognitive data processing and the effectiveness of the soldiers or decision-making commanders. This article summarises the most recent results of the radar-related research, taking into account domestic and Eastern European expectations.

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