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Prof. Dr. Michael K Ng

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Institute of Data Science, Department of Mathematics, The University of Hong Kong, Hong Kong, China
Interests: applied and computational mathematics; artificial intelligence and machine learning; data and imaging science

Special Issue Information

Dear Colleagues,

Signal processing is an electrical engineering subfield that focuses on analyzing, modifying, and synthesizing signals, such as sound, images, and scientific measurements. Remote Sensing data can be multidimensional signals, hyperspectral images, time series, radar/Lidar data, GNSS, and video sequences, which could be present and real-time. The main goal of the special issue collection is to address advanced topics related to signal processing, image processing, machine learning, computer vision, and pattern recognition in the Remote Sensing field.

Prof. Dr. Michael K Ng
Guest Editor

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Keywords

signal and image processing
signal and image analysis
AI and machine learning
hyperspectral images
remote sensing

Published Papers (1 paper)

Research

27 pages, 8589 KiB

Open AccessArticle

A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation

by Pengfei Liu, Lishu Guo, Hang Zhao, Peng Shang, Ziyue Chu and Xiaochun Lu

Remote Sens. 2023, 15(21), 5214; https://doi.org/10.3390/rs15215214 - 02 Nov 2023

Viewed by 725

Abstract

Specific emitter identification (SEI) is a professional technology to recognize different emitters by measuring the unique features of received signals. It has been widely used in both civilian and military fields. Recently, many SEI methods based on deep learning have been proposed, most of which assume that the training set and testing set have the same data distribution. However, in reality, the testing set is generally used later than the training set and lacks labels. The long time span may change the signal transmission environment and fingerprint features. These changes result in considerable differences in data distribution between the training and testing sets, thereby affecting the recognition and prediction abilities of the model. Therefore, the existing works cannot achieve satisfactory results for a long time span SEI. To address this challenge and obtain stable fingerprints, we transform the long time span SEI problem into a domain adaptive problem and propose an unsupervised domain adaptive method called LTS-SEI. Noteworthily, LTS-SEI uses a multilayer convolutional feature extractor to learn feature knowledge and confronts a domain discriminator to generate domain-invariant shallow fingerprints. The classifier of LTS-SEI applies feature matching to source domain samples and target domain samples to achieve the domain alignment of deep fingerprints. The classifier further reduces the intraclass diversity of deep features to alleviate the misclassification problem of edge samples in the target domain. To confirm the effectiveness and reliability of LTS-SEI, we collect multiple sets of real satellite navigation signals using two antennas with 13 m- and 40 m-large apertures, respectively, and construct two available datasets. Numerous experiments demonstrate that LTS–SEI can considerably increase the recognition accuracy of the long time span SEI and is superior to the other existing methods. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Advances on Signal and Image Processing for Remote Sensing)

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