Neural Networks &amp; Machine Learning in Cognitive Radar

Smith, Gerald Birney; Gürbüz, Sevgi Zübeyde; Bruggenwirth, Stefan; John-Baptiste, Peter

doi:10.1109/radarconf2043947.2020.9266670

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…For example, in [16], a non-linear transformation-based machine learning approach is used for adaptively adjusting the detection threshold. In [17], different aspects of cognition implemented through neural networks are discussed. Reinforcement learning (RL) is also a popular method to introduce cognition into radar systems.…”

Section: Of 22mentioning

confidence: 99%

RL-Based Detection, Tracking, and Classification of Malicious UAV Swarm through Airborne Cognitive Multibeam Multifunction Phased Array Radar

Khawaja¹,

Yaqoob²,

Güvenç³

2023

Preprint

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Detecting, tracking, and classifying unmanned aerial vehicles (UAVs) in a swarm presents significant challenges due to their small and diverse radar cross-sections, multiple flight altitudes, velocities, and close trajectories. To overcome these challenges, adjustments of the radar parameters and/or position of the radar (for airborne platforms) are often required during runtime. The runtime adjustments help to overcome the anomalies in the detection, tracking, and classification of UAVs. The runtime adjustments are performed either manually or through fixed algorithms, each of which can have its limitations for complex and dynamic scenarios. In this work, we propose the use of multi-agent reinforcement learning (RL) to carry out the runtime adjustment of the radar parameters and position of the radar platform. The radar used in our work is a multibeam multifunction phased array radar (MMPAR) placed onboard UAVs. The simulations show the cognitive adjustment of the MMPAR parameters and position of the airborne platform using RL helps to overcome anomalies in the detection, tracking, and classification of UAVs in a swarm. A comparison with other artificial intelligence (AI) algorithms shows that RL performs better due to runtime learning of the environment through rewards.

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Section: Of 22mentioning

confidence: 99%

RL-Based Detection, Tracking, and Classification of Malicious UAV Swarm through Airborne Cognitive Multibeam Multifunction Phased Array Radar

Khawaja¹,

Yaqoob²,

Güvenç³

2023

Preprint

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“…The discussion in ref. [100] provides a generic overview on application of neural networks and ML in the development of cognitive radars with a motive to reduce the computational cost for real-time implementation. The paper discusses a use case scenario where RL is employed to generate waveforms with a 26 dB power spectral density (PSD) notch.…”

Section: Waveform Synthesis and Selectionmentioning

confidence: 99%

Artificial intelligence meets radar resource management: A comprehensive background and literature review

Hashmi

Akbar

Adve

et al. 2022

IET Radar Sonar & Navi

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A multi‐function radar is designed to perform disparate functions, such as surveillance, tracking, fire control, amongst others, within a limited resource (time, frequency, and energy) budget. A radar resource management (RRM) module within a radar system makes decisions on prioritisation, parameter selection, and scheduling of associated tasks. However, optimal RRM algorithms are generally computationally complex and operational radars resort to heuristics. On the other hand, algorithms based on artificial intelligence (AI) have been shown to yield near‐optimal radar resource allocation results at manageable computational complexity. This survey study aims at enabling researchers and practitioners better understand the application of AI in RRM‐related problems by providing a thorough literature review of AI‐based RRM techniques. We first provide background concepts in RRM followed by a brief review of Symbolic‐AI techniques for RRM. We mainly focus on the applications of state‐of‐the‐art machine learning techniques to RRM. We emphasise on the recent findings and their potential within practical RRM scenarios for real‐time resource allocation optimisation. The study concludes with a discussion of open research problems and future research directions in the light of the presented survey.

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“…For example, in [15], a non-linear transformation-based machine learning approach is used for adaptively adjusting the detection threshold. In [16], different aspects of cognition implemented through neural networks are discussed. Reinforcement learning (RL) is also a popular method to introduce cognition into radar systems.…”

Section: Introductionmentioning

confidence: 99%

RL-Based Detection, Tracking, and Classification of Malicious UAV Swarms through Airborne Cognitive Multibeam Multifunction Phased Array Radar

Khawaja

Yaqoob

Güvenç

2023

Drones

View full text Add to dashboard Cite

Detecting, tracking, and classifying unmanned aerial vehicles (UAVs) in a swarm presents significant challenges due to their small and diverse radar cross-sections, multiple flight altitudes, velocities, and close trajectories. To overcome these challenges, adjustments of the radar parameters and/or position of the radar (for airborne platforms) are often required during runtime. The runtime adjustments help to overcome the anomalies in the detection, tracking, and classification of UAVs. The runtime adjustments are performed either manually or through fixed algorithms, each of which can have its limitations for complex and dynamic scenarios. In this work, we propose the use of multi-agent reinforcement learning (RL) to carry out the runtime adjustment of the radar parameters and position of the radar platform. The radar used in our work is a multibeam multifunction phased array radar (MMPAR) placed onboard UAVs. The simulations show that the cognitive adjustment of the MMPAR parameters and position of the airborne platform using RL helps to overcome anomalies in the detection, tracking, and classification of UAVs in a swarm. A comparison with other artificial intelligence (AI) algorithms shows that RL performs better due to the runtime learning of the environment through rewards.

show abstract

Neural Networks & Machine Learning in Cognitive Radar

Cited by 6 publications

References 15 publications

RL-Based Detection, Tracking, and Classification of Malicious UAV Swarm through Airborne Cognitive Multibeam Multifunction Phased Array Radar

RL-Based Detection, Tracking, and Classification of Malicious UAV Swarm through Airborne Cognitive Multibeam Multifunction Phased Array Radar

Artificial intelligence meets radar resource management: A comprehensive background and literature review

RL-Based Detection, Tracking, and Classification of Malicious UAV Swarms through Airborne Cognitive Multibeam Multifunction Phased Array Radar

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