A hybrid convolutional neural network with fusion of handcrafted and deep features for FHSS signals classification

Khan, Muhammad Turyalai; Sheikh, Usman Ullah

doi:10.1016/j.eswa.2023.120153

Cited by 7 publications

(1 citation statement)

References 25 publications

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“…Nowadays, with the outstanding computational power of graphics processing units (GPUs), deep learning technology has also been widely adopted in signal processing areas, including signal feature extractions [36], classifications [37], and parameter estimations [38]. In these implementations, the trained deep neural networks (DNNs) showed high efficiency and outstanding signal analysis capabilities [39,40].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Joint Carrier and DOA Estimations of FHSS Signals Based on Knowledge-Enhanced Compressed Measurements and Deep Learning

Jiang,

Liu

2024

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As one of the most widely used spread spectrum techniques, the frequency-hopping spread spectrum (FHSS) has been widely adopted in both civilian and military secure communications. In this technique, the carrier frequency of the signal hops pseudo-randomly over a large range, compared to the baseband. To capture an FHSS signal, conventional non-cooperative receivers without knowledge of the carrier have to operate at a high sampling rate covering the entire FHSS hopping range, according to the Nyquist sampling theorem. In this paper, we propose an adaptive compressed method for joint carrier and direction of arrival (DOA) estimations of FHSS signals, enabling subsequent non-cooperative processing. The compressed measurement kernels (i.e., non-zero entries in the sensing matrix) have been adaptively designed based on the posterior knowledge of the signal and task-specific information optimization. Moreover, a deep neural network has been designed to ensure the efficiency of the measurement kernel design process. Finally, the signal carrier and DOA are estimated based on the measurement data. Through simulations, the performance of the adaptively designed measurement kernels is proved to be improved over the random measurement kernels. In addition, the proposed method is shown to outperform the compressed methods in the literature.

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