Automatic radar waveform recognition for integrated sensing and communication systems

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master_Batista_de_Sousa_Correia_da_Costa_Diogo_2025.pdf (24.05 MB)

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School of Electrical Engineering | Master's thesis
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Date

2025-09-10

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Major/Subject

Communications Engineering and Data Science

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Degree programme

Joint International Master's Programme in Communications and Data Science
Joint International Master's Programme in Communications and Data Science
Joint International Master's Programme in Communications and Data Science

Language

en

Pages

77

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Abstract

The spectrum is a shared, congested, and densely used resource. The emerging sixth-generation wireless (6G) systems intend to alleviate this problem by integrating radio frequency sensing and wireless communications functionalities, enabling spectrum, hardware, and antenna resource sharing. Agile spectrum use requires situational awareness (SA) about the state of the radio environment. Automatic waveform recognition (AWR) is an important part of building and maintaining SA needed in cognitive radios, radars, and signal intelligence. Commonly, a convolutional neural network (CNN) has been paired with time-frequency (TF) imaging to distinguish among different low probability of intercept/detect (LPI/LPD) waveforms. In this thesis, a thorough investigation of multiple time-frequency representations (TFRs) is conducted, including linear, bilinear, and nonlinear representations. The effects of TF resolution in AWR recognition are investigated, and multiple downsampling strategies of the time-frequency images (TFIs) in the frequency and time domains are compared. Practical classification results are presented under different sensing channel conditions, with the analysis encompassing both standard additive white Gaussian noise (AWGN) channels and more realistic multipath fading channels.

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Supervisor

Koivunen, Visa

Thesis advisor

Figueiredo, Mário

Keywords

automatic radar waveform recognition, deep learning, integrated sensing and communications, LPI/LPD radar, time-frequency imaging, convulotional neural networks

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https://urn.fi/URN:NBN:fi:aalto-202512169331

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