Underwater localization with binary measurements: From compressed sensing to deep unfolding

Onderzoeksoutput: Articlepeer review


1-bit compressive sensing (1-bit CS) refers to reconstructing a sparse signal from the sign of its measurements. Unlike compressive sensing measurements, the binary measurements in 1-bit CS can be realized using a simple comparator, thereby reducing the hardware complexity and the cost of the receiving array. Many recovery algorithms have been proposed for 1-bit CS, including recently methods based on deep learning. In this paper, we design a new method for underwater source localization by combining the matched field processing method (MFP) with 1-bit CS. We use the Fixed Point Continuation (FPC) method and a deep neural network designed by unfolding its iterations to solve the 1-bit recovery problem and evaluate their performance in the associated source localization problem. Furthermore, in order to improve the robustness of the signal recovery to noise added in the binary measurements, we propose to preprocess the received data with a simple average technique; this formulates the proposed Average FPC-ℓ1 (AVG-FPC-ℓ1) method. Our experiments show that an underwater target can be successfully located by using the sign of the measurements. Moreover, with the aid of the noise immunity method and the deep neural network (DNN) structure, the accuracy of the location estimates is greatly improved.

Originele taal-2English
Aantal pagina's13
TijdschriftDigital Signal Processing
StatusPublished - mrt 2023

Bibliografische nota

Funding Information:
This research received funding from the Flemish Government under the “Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen” programme and from the FWO (Projects G040016N and G0A4720N ). This research was also supported by the National Natural Science Foundation of China under Grants 61901273 , in part by the Stable Supporting Fund of Acoustics Science and Technology Laboratory and in part by the Youth Innovation Promotion Association CAS under Grants 2021023 .

Publisher Copyright:
© 2022 Elsevier Inc.

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