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Remote Sensing
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Radar and Microwave Sensor Systems: Technology and Applications
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Radar and Microwave Sensor Systems: Technology and Applications

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (1 December 2023) | Viewed by 6602

Special Issue Editors

Prof. Dr. Ronghua Shang

E-Mail Website
Guest Editor
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence, Xidian University, Xi’an 710071, China
Interests: evolutionary computation; image processing; data mining
Dr. Pia Addabbo

E-Mail Website
Guest Editor
Department of Telecommunication Engineering, University of Study “Giustino Fortunato”, 82100 Benevento, Italy
Interests: statistical signal processing applied to radar target recognition global navigation satellite system reflectometry, and hyperspectral unmixing; elaboration of satellite data for Earth observation with application in imaging and sounding with passive (multispectral and hyperspectral) and active (SAR, GNSS-R) sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensor technology, which is currently of great significance, originated in the 1950s. Like the "ear" and "eye", sensors have powerful information-acquisition capabilities, and they play an important role in radar. As one of the most popular types, microwave sensors can work continuously in all weather conditions, and have the ability to penetrate ice, snow, forest and soil. With fast imaging, microwave sensors can receive microwave radiation with a wavelength of 1mm~30cm; the corresponding images cover large areas, and the targets are clear and recognizable. Synthetic aperture radar (SAR), equipped with a microwave sensor, is an active Earth observation system, and has been carried on aircrafts, satellites and other flight platforms. SAR has been widely used in resource exploration, disaster assessment, military mapping and other fields; this is mainly because a microwave’s wider spectral band provides SAR images with rich information, making it appropriate for high-level applications.

This Special Issue welcomes studies on the processing and interpretation of data from radars with microwave sensors. Topics may cover any topic, from land cover classification or segmentation to more comprehensive aims and scales. We also welcome studies on military target detection, SAR image feature extraction and SAR image denoising. Articles may address, but are not limited to, the following topics:

  • 3-D target reconstruction;
  • Land cover segmentation;
  • Land cover classification;
  • Change detection;
  • Target recognition;
  • Image denoising;
  • Terrain analysis;
  • Flood detection;
  • Sea ice concentration estimation;
  • Geomorphologic extraction of an active volcano;
  • Bridge thermal dilation monitoring.

Prof. Dr. Ronghua Shang
Dr. Pia Addabbo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • SAR image
  • microwave sensor
  • segmentation
  • terrain analysis
  • classification
  • denoising
  • reconstruction

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Published Papers (4 papers)

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Research

20 pages, 8019 KiB  
Article
An Embedded-GPU-Based Scheme for Real-Time Imaging Processing of Unmanned Aerial Vehicle Borne Video Synthetic Aperture Radar
by Tao Yang, Xinyu Zhang, Qingbo Xu, Shuangxi Zhang and Tong Wang
Remote Sens. 2024, 16(1), 191; https://doi.org/10.3390/rs16010191 - 2 Jan 2024
Cited by 1 | Viewed by 1890
Abstract
The UAV-borne video SAR (ViSAR) imaging system requires miniaturization, low power consumption, high frame rates, and high-resolution real-time imaging. In order to satisfy the requirements of real-time imaging processing for the UAV-borne ViSAR under limited memory and parallel computing resources, this paper proposes [...] Read more.
The UAV-borne video SAR (ViSAR) imaging system requires miniaturization, low power consumption, high frame rates, and high-resolution real-time imaging. In order to satisfy the requirements of real-time imaging processing for the UAV-borne ViSAR under limited memory and parallel computing resources, this paper proposes a method of embedded GPU-based real-time imaging processing for the UAV-borne ViSAR. Based on a parallel programming model of the compute unified device architecture (CUDA), this paper designed a parallel computing method for range-Doppler (RD) and map drift (MD) algorithms. By utilizing the advantages of the embedded GPU characterized with parallel computing, we improved the processing speed of real-time ViSAR imaging. This paper also adopted a unified memory management method, which greatly reduces data replication and communication latency between the CPU and the GPU. The data processing of 2048 × 2048 points took only 1.215 s on the Jetson AGX Orin platform to form a nine-consecutive-frame image with a resolution of 0.15 m, with each frame taking only 0.135 s, enabling real-time imaging at a high frame rate of 5 Hz. In actual testing, continuous mapping can be achieved without losing the scenes, intuitively obtaining the dynamic observation effects of the area. The processing results of the measured data have verified the reliability and effectiveness of the proposed scheme, satisfying the processing requirements for real-time ViSAR imaging. Full article
(This article belongs to the Special Issue Radar and Microwave Sensor Systems: Technology and Applications)
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18 pages, 8991 KiB  
Communication
Linear Frequency Modulation and Orthogonal Code Modulation for Co-Located Multiple-Input Multiple-Output High-Frequency Surface Wave Radar
by Eunhee Kim, Sunghwan Sohn, Hyunwook Moon, Jun Hyeok Choi and Kiwon Lee
Remote Sens. 2024, 16(1), 104; https://doi.org/10.3390/rs16010104 - 26 Dec 2023
Cited by 2 | Viewed by 1239
Abstract
A high-frequency surface wave radar (HFSWR) is the only sensor that provides inexpensive surveillance for up to 200 nautical miles (NM) of the exclusive economic zone in the 3–5 MHz band. However, because of its long wavelength, its angular resolution is low. Multiple-input [...] Read more.
A high-frequency surface wave radar (HFSWR) is the only sensor that provides inexpensive surveillance for up to 200 nautical miles (NM) of the exclusive economic zone in the 3–5 MHz band. However, because of its long wavelength, its angular resolution is low. Multiple-input multiple-output (MIMO) technology is an attractive method to improve angular resolution. This paper proposes MIMO waveforms and their processing that can be used in HFSWR systems. This dual modulation method applies linear frequency modulation to each pulse and orthogonal polyphase codes for a few consecutive pulses to enable MIMO processing. The proposed method can effectively remove the correlation of mutual interference and exhibits excellent performance in removing multiple-time-around echoes. Full article
(This article belongs to the Special Issue Radar and Microwave Sensor Systems: Technology and Applications)
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17 pages, 1173 KiB  
Article
Multi-Hypothesis Marginal Multi-Target Bayes Filter for a Heavy-Tailed Observation Noise
by Zongxiang Liu, Junwen Luo and Chunmei Zhou
Remote Sens. 2023, 15(21), 5258; https://doi.org/10.3390/rs15215258 - 6 Nov 2023
Cited by 1 | Viewed by 1103
Abstract
A multi-hypothesis marginal multi-target Bayes filter for heavy-tailed observation noise is proposed to track multiple targets in the presence of clutter, missed detection, and target appearing and disappearing. The proposed filter propagates the existence probabilities and probability density functions (PDFs) of targets in [...] Read more.
A multi-hypothesis marginal multi-target Bayes filter for heavy-tailed observation noise is proposed to track multiple targets in the presence of clutter, missed detection, and target appearing and disappearing. The proposed filter propagates the existence probabilities and probability density functions (PDFs) of targets in the filter recursion. It uses the Student’s t distribution to model the heavy-tailed non-Gaussian observation noise, and employs the variational Bayes technique to acquire the approximate distributions of individual targets. K-best hypotheses, obtained by minimizing the negative log-generalized-likelihood ratio, are used to establish the existence probabilities and PDFs of targets in the filter recursion. Experimental results indicate that the proposed filter achieves better tracking performance than other filters. Full article
(This article belongs to the Special Issue Radar and Microwave Sensor Systems: Technology and Applications)
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Graphical abstract

22 pages, 979 KiB  
Article
Student’s t-Based Robust Poisson Multi-Bernoulli Mixture Filter under Heavy-Tailed Process and Measurement Noises
by Jiangbo Zhu, Weixin Xie and Zongxiang Liu
Remote Sens. 2023, 15(17), 4232; https://doi.org/10.3390/rs15174232 - 29 Aug 2023
Cited by 6 | Viewed by 1089
Abstract
A novel Student’s t-based robust Poisson multi-Bernoulli mixture (PMBM) filter is proposed to effectively perform multi-target tracking under heavy-tailed process and measurement noises. To cope with the common scenario where the process and measurement noises possess different heavy-tailed degrees, the proposed filter models [...] Read more.
A novel Student’s t-based robust Poisson multi-Bernoulli mixture (PMBM) filter is proposed to effectively perform multi-target tracking under heavy-tailed process and measurement noises. To cope with the common scenario where the process and measurement noises possess different heavy-tailed degrees, the proposed filter models this noise as two Student’s t-distributions with different degrees of freedom. Furthermore, this method considers that the scale matrix of the one-step predictive probability density function is unknown and models it as an inverse-Wishart distribution to mitigate the influence of heavy-tailed process noise. A closed-form recursion of the PMBM filter for propagating the approximated Gaussian-based PMBM posterior density is derived by introducing the variational Bayesian approach and a hierarchical Gaussian state-space model. The overall performance improvement is demonstrated through three simulations. Full article
(This article belongs to the Special Issue Radar and Microwave Sensor Systems: Technology and Applications)
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