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DNCNet: deep radar signal denoising and recognition

DNCNet: deep radar signal denoising and recognition
DNCNet: deep radar signal denoising and recognition

Deep learning with its rapid development and advancement has achieved unparalleled performance in many areas like computer vision as well as cognitive radio and signal recognition. However, the performance of most deep neural networks would suffer from degradation in the data mismatch scenario, e.g., the test dataset has a related but nonidentical distribution with the training dataset. Considering the noise corruption, a classifier's accuracy might drop sharply when it is tested on a dataset with much lower signal-to-noise ratio compared to its training dataset. To address this dilemma, in this work, we propose an efficient denoising and classification network (DNCNet) for radar signals. The DNCNet consists of denoising and classification subnetworks. First, a radar signal detection and synthetic mechanism is designed to generate pairwise clean data and noisy data for the DNCNet to train its denoising subnetwork. Then, a two-phase training procedure is proposed to train the denoising subnetwork in the first phase and strengthen the mapping between the denoising results and perceptual representation in the second. Experiments on synthetic and benchmark datasets validate the excellent performance of the proposed DNCNet against state-of-the-art methods in terms of both signal restoration quality and classification accuracy.

Deep learning, denoising, neural network, radar emitter recognition, radio signal
3549-3562
Du, Mingyang
d42b1519-40d9-476a-b2a3-2b9f6c63d46a
Zhong, Ping
a51c7669-d590-4a7b-adc8-0f7d2b9e1932
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Bi, Daping
7d0942b5-14a9-4e09-b709-416fa34f31a2
Du, Mingyang
d42b1519-40d9-476a-b2a3-2b9f6c63d46a
Zhong, Ping
a51c7669-d590-4a7b-adc8-0f7d2b9e1932
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Bi, Daping
7d0942b5-14a9-4e09-b709-416fa34f31a2

Du, Mingyang, Zhong, Ping, Cai, Xiaohao and Bi, Daping (2022) DNCNet: deep radar signal denoising and recognition. IEEE Transactions on Aerospace and Electronic Systems, 58 (4), 3549-3562. (doi:10.1109/TAES.2022.3153756).

Record type: Article

Abstract

Deep learning with its rapid development and advancement has achieved unparalleled performance in many areas like computer vision as well as cognitive radio and signal recognition. However, the performance of most deep neural networks would suffer from degradation in the data mismatch scenario, e.g., the test dataset has a related but nonidentical distribution with the training dataset. Considering the noise corruption, a classifier's accuracy might drop sharply when it is tested on a dataset with much lower signal-to-noise ratio compared to its training dataset. To address this dilemma, in this work, we propose an efficient denoising and classification network (DNCNet) for radar signals. The DNCNet consists of denoising and classification subnetworks. First, a radar signal detection and synthetic mechanism is designed to generate pairwise clean data and noisy data for the DNCNet to train its denoising subnetwork. Then, a two-phase training procedure is proposed to train the denoising subnetwork in the first phase and strengthen the mapping between the denoising results and perceptual representation in the second. Experiments on synthetic and benchmark datasets validate the excellent performance of the proposed DNCNet against state-of-the-art methods in terms of both signal restoration quality and classification accuracy.

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DNCNet_ Deep Radar Signal Denoising and Recognition - Accepted Manuscript
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More information

Accepted/In Press date: 20 February 2022
e-pub ahead of print date: 25 February 2022
Published date: 1 August 2022
Keywords: Deep learning, denoising, neural network, radar emitter recognition, radio signal

Identifiers

Local EPrints ID: 456581
URI: http://eprints.soton.ac.uk/id/eprint/456581
PURE UUID: c8b95970-bb12-4bfe-b6c3-750f5c0dbb30
ORCID for Xiaohao Cai: ORCID iD orcid.org/0000-0003-0924-2834

Catalogue record

Date deposited: 05 May 2022 16:39
Last modified: 18 Oct 2022 01:58

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Contributors

Author: Mingyang Du
Author: Ping Zhong
Author: Xiaohao Cai ORCID iD
Author: Daping Bi

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