Analysis of machine learning methods for clutter classification

Washington, Richard; Garmatyuk, Dmitriy; Mudaliar, Saba; Narayanan, Ram M.

doi:10.1117/12.2588310

Cited by 2 publications

(2 citation statements)

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“…The simplest form of motion filtering or clutter rejection for pulse-based radar systems is obtained by subtracting the instantaneous CIR from the mean CIR [89]. In [101], a machine learning approach is used for the classification and modeling of clutter in the presence of interference and noise. Orthogonal frequency-division multiple access is used for the collection of clutter signals and joint radar sensing and communications, and machine learning is applied to obtain a classification accuracy of 79%.…”

Section: E Motion Filteringmentioning

confidence: 99%

A Survey on Radar Techniques for Detection,Tracking, and Classification of Aerial Threats

Khawaja¹

2022

Preprint

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<p> The use of unmanned aerial vehicles (UAVs) for different applications has increased many folds in recent years. The UAVs are expected to change the future air operations. However, there are instances where the UAVs can be used for malicious purposes. The detection, tracking, and classification of UAVs is challenging compared to manned aerial vehicles (MAVs) mainly due to small size, complex shapes, and ability to fly close to the terrain and in autonomous flight patterns in swarms. In this survey, we will discuss current and future aerial threats, and provide an overview of radar systems to counter such threats. We also study the performance parameters of radar systems for the detection, tracking, and classification of UAVs compared to MAVs. Finally, limitations of radar systems and comparison with other techniques that do not rely on radars for detection, tracking, and classification of aerial threats are also provided. </p>

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Section: E Motion Filteringmentioning

confidence: 99%