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Article

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

1
College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China
2
Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Nankai University, Tianjin 300350, China
*
Author to whom correspondence should be addressed.
Entropy 2024, 26(7), 544; https://doi.org/10.3390/e26070544
Submission received: 2 May 2024 / Revised: 18 June 2024 / Accepted: 20 June 2024 / Published: 26 June 2024

Abstract

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.
Keywords: knowledge-enhanced compressed measurements; FHSS; carrier estimation; direction-of-arrival estimation; deep learning knowledge-enhanced compressed measurements; FHSS; carrier estimation; direction-of-arrival estimation; deep learning

Share and Cite

MDPI and ACS Style

Jiang, Y.; Liu, F. Adaptive Joint Carrier and DOA Estimations of FHSS Signals Based on Knowledge-Enhanced Compressed Measurements and Deep Learning. Entropy 2024, 26, 544. https://doi.org/10.3390/e26070544

AMA Style

Jiang Y, Liu F. Adaptive Joint Carrier and DOA Estimations of FHSS Signals Based on Knowledge-Enhanced Compressed Measurements and Deep Learning. Entropy. 2024; 26(7):544. https://doi.org/10.3390/e26070544

Chicago/Turabian Style

Jiang, Yinghai, and Feng Liu. 2024. "Adaptive Joint Carrier and DOA Estimations of FHSS Signals Based on Knowledge-Enhanced Compressed Measurements and Deep Learning" Entropy 26, no. 7: 544. https://doi.org/10.3390/e26070544

APA Style

Jiang, Y., & Liu, F. (2024). Adaptive Joint Carrier and DOA Estimations of FHSS Signals Based on Knowledge-Enhanced Compressed Measurements and Deep Learning. Entropy, 26(7), 544. https://doi.org/10.3390/e26070544

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