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Remote Sensing
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Innovative Applications of HF Radar
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Innovative Applications of HF Radar

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

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 8750

Special Issue Editors

Prof. Dr. Yonggang Ji

E-Mail Website
Guest Editor
College of Oceanography and Space Informatics, China University of Petroleum(East China), No. 66 West Changjiang Road, Qingdao 266580, China
Interests: remote sensing of HF radar; maritime vessel targets monitoring with multiple remote sensors; shipborne HF surface wave technology
Special Issues, Collections and Topics in MDPI journals
Dr. Yiming Wang

E-Mail Website
Guest Editor
Laboratory of Marine Physics and Remote Sensing, First Institute of Oceanography, Ministry of Natural Resources, No. 6 Xianxialing Road, Qingdao 266061, China
Interests: clutter suppression of HF radar; HF radar signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Weifeng Sun

E-Mail Website
Guest Editor
College of Oceanography and Space Informatics, China University of Petroleum(East China), No. 66, West Changjiang Road, Qingdao 266580, China
Interests: target detection and tracking with compact HFSWR; signal and image processing algorithms and their applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Weimin Huang

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Memorial University, St. John’s, NL A1B 3X5, Canada
Interests: mapping of oceanic surface parameters via high-frequency ground wave radar; X-band marine radar and global navigation satellite systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

HF radar, including both HF surface wave radar and HF skywave over-the-horizon radar, is primarily employed for detecting and tracking maritime targets as well as monitoring sea state parameters like ocean currents, wind, and waves. HF radar boasts advantages such as wide coverage, long-range, and real-time monitoring capabilities. It has found extensive applications in diverse fields, including maritime security, maritime traffic and fishery management, and maritime disaster monitoring, thereby contributing to the safeguarding of maritime interests and the safety of maritime activities. Additionally, HF radar can be employed to monitor oceanic dynamic processes, including oceanic circulation and mesoscale eddies as well as oceanic disasters like typhoons and tsunamis. In recent years, a variety of new HF radar systems, such as bistatic HF radar, ship-borne or buoy-based, sky-wave transmitting and shore/ship-receiving systems, and MIMO systems have emerged, further expanding the detection range and application fields of HF radar.

In this Special Issue, we would like to focus on the innovative applications of HF radar, particularly those related to various new HF radar systems that have emerged in recent years. These innovative applications encompass not only new investigations directly related to target detection and sea-state inversion with HF radar, but also research on signal processing and data quality evaluation of HF radar products. Additionally, we also aim to explore the new applications of HF radar in different industries and various application environments. This includes investigating how HF radar can be utilized in fields such as maritime surveillance, coastal management, weather monitoring, and environmental monitoring.  By delving into these innovative applications, we hope to reveal the versatility of HF radar technology and its potential to address challenges in a wide range of sectors and settings. Moreover, through the study of these innovative applications, we strive to further explore the untapped potential of HF radar technology and expand its application fields in the realm of ocean remote sensing.

Articles may address, but are not limited, to the following topics related to HF radar:

  • New HF radar system;
  • Radar signal processing;
  • Clutter suppression;
  • Target detection and tracking;
  • Wind and wave monitoring;
  • High-frequency radar data processing and analysis;
  • Data product quality evaluation;
  • Marine hazard monitoring;
  • Monitoring of ocean dynamical processes;
  • Ocean circulation monitoring.

Prof. Dr. Yonggang Ji
Dr. Yiming Wang
Dr. Weifeng Sun
Prof. Dr. Weimin Huang
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

  • HF radar
  • target detection
  • target tracking
  • sea state inversion
  • OTH radar
  • clutter suppression

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Related Special Issue

Published Papers (6 papers)

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Research

16 pages, 5632 KiB  
Article
Ship Formation Identification with Spatial Features and Deep Learning for HFSWR
by Jiaqi Wang, Aijun Liu, Changjun Yu and Yuanzheng Ji
Remote Sens. 2024, 16(3), 577; https://doi.org/10.3390/rs16030577 - 2 Feb 2024
Cited by 1 | Viewed by 1429
Abstract
Ship detection has been an area of focus for high-frequency surface wave radar (HFSWR). The detection and identification of ship formation have proven significant in early warning, while studies on the formation identification are limited due to the complex background and low resolution [...] Read more.
Ship detection has been an area of focus for high-frequency surface wave radar (HFSWR). The detection and identification of ship formation have proven significant in early warning, while studies on the formation identification are limited due to the complex background and low resolution of HFSWR. In this paper, we first establish a spatial distribution model of ship formation in HFSWR. Then, we propose a cascade identification algorithm of ship formation in the clutter edge. The proposed algorithm includes a preprocessing stage and a two-stage formation identification stage. The Faster R-CNN is introduced in the preprocessing stage to locate the clutter regions. In the first stage, we propose an extremum detector based on connected regions to extract suspicious regions. The suspicious regions contain ship formations, single-ship targets, and false targets. In the second stage, we design a network connected by a convolutional neural network (CNN) and an extreme learning machine (ELM) to identify two densely distributed ship formations from inhomogeneous clutter and single-ship targets. The experimental results based on the factual HFSWR background demonstrate that the proposed cascade identification algorithm is superior to the extremum detector combined with the classical CNN algorithm for ship formation identification. Meanwhile, the proposed algorithm performs well in weak formation and deformed formation identification. Full article
(This article belongs to the Special Issue Innovative Applications of HF Radar)
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29 pages, 24728 KiB  
Article
Target Detection Method for High-Frequency Surface Wave Radar RD Spectrum Based on (VI)CFAR-CNN and Dual-Detection Maps Fusion Compensation
by Yuanzheng Ji, Aijun Liu, Xuekun Chen, Jiaqi Wang and Changjun Yu
Remote Sens. 2024, 16(2), 332; https://doi.org/10.3390/rs16020332 - 14 Jan 2024
Cited by 5 | Viewed by 1559
Abstract
This paper proposes a method for the intelligent detection of high-frequency surface wave radar (HFSWR) targets. This method cascades the adaptive constant false alarm (CFAR) detector variability index (VI) with the convolutional neural network (CNN) to form a cascade detector (VI)CFAR-CNN. First, the [...] Read more.
This paper proposes a method for the intelligent detection of high-frequency surface wave radar (HFSWR) targets. This method cascades the adaptive constant false alarm (CFAR) detector variability index (VI) with the convolutional neural network (CNN) to form a cascade detector (VI)CFAR-CNN. First, the (VI)CFAR algorithm is used for the first-level detection of the range–Doppler (RD) spectrum; based on this result, the two-dimensional window slice data are extracted using the window with the position of the target on the RD spectrum as the center, and input into the CNN model to carry out further target and clutter identification. When the detection rate of the detector reaches a certain level and cannot be further improved due to the convergence of the CNN model, this paper uses a dual-detection maps fusion method to compensate for the loss of detection performance. First, the optimized parameters are used to perform the weighted fusion of the dual-detection maps, and then, the connected components in the fused detection map are further processed to achieve an independent (VI)CFAR to compensate for the (VI)CFAR-CNN detection results. Due to the difficulty in obtaining HFSWR data that include comprehensive and accurate target truth values, this paper adopts a method of embedding targets into the measured background to construct the RD spectrum dataset for HFSWR. At the same time, the proposed method is compared with various other methods to demonstrate its superiority. Additionally, a small amount of automatic identification system (AIS) and radar correlation data are used to verify the effectiveness and feasibility of this method on completely measured HFSWR data. Full article
(This article belongs to the Special Issue Innovative Applications of HF Radar)
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20 pages, 11823 KiB  
Article
Joint Analysis and Morphological Characterization of HFSWR Echo Properties during Severe Typhoon Muifa
by Rong Wang, Zhe Lyu, Changjun Yu, Aijun Liu and Taifan Quan
Remote Sens. 2024, 16(2), 267; https://doi.org/10.3390/rs16020267 - 10 Jan 2024
Cited by 1 | Viewed by 987
Abstract
Investigating the dynamic evolution process of the ocean and ionosphere in sudden sea conditions poses a challenging problem. To address this objective, this study utilizes actual data from high-frequency surface wave radar (HFSWR) to analyze, validate, summarize, and characterize the echo properties of [...] Read more.
Investigating the dynamic evolution process of the ocean and ionosphere in sudden sea conditions poses a challenging problem. To address this objective, this study utilizes actual data from high-frequency surface wave radar (HFSWR) to analyze, validate, summarize, and characterize the echo properties of the ocean and ionosphere during the severe Typhoon Muifa. By employing the short-time Fourier transform (STFT) method, the HFSWR ocean and ionosphere echoes stimulated by typhoon-induced gravity waves are observed, and the joint gravity wave features of the ocean and ionosphere echoes at different time scales are extracted. Additionally, the phase-space reconstruction method is employed to characterize the dynamical evolution of the joint gravity wave features in higher-dimensional space. Furthermore, the chaotic dynamics behavior of the joint gravity wave features is analyzed using the largest Lyapunov exponents. By combining the gravity wave features with chaotic dynamics, this study introduces a method to characterize the joint gravity wave features. The extraction of joint gravity wave features in HFSWR echoes stimulated by typhoons, along with the construction of a chaotic characterization scheme for the gravity wave features, provides an innovative approach and a solid technical foundation for studying the ocean and ionosphere using HFSWR under sudden sea conditions. Full article
(This article belongs to the Special Issue Innovative Applications of HF Radar)
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22 pages, 9268 KiB  
Article
Improved Main Lobe Cancellation Method for Suppression Directional Noise in HFSWR Systems
by Dezhu Xiao, Xin Zhang, Qiang Yang and Jiaming Li
Remote Sens. 2024, 16(2), 254; https://doi.org/10.3390/rs16020254 - 9 Jan 2024
Viewed by 1107
Abstract
High frequency surface wave radar (HFSWR) has been successfully developed for early warning, especially for vessel target detection. However, the system’s performance is consistently constrained by external environmental noise, particularly directional noise, which presents a new problem for HFSWR. Anisotropic directional noise has [...] Read more.
High frequency surface wave radar (HFSWR) has been successfully developed for early warning, especially for vessel target detection. However, the system’s performance is consistently constrained by external environmental noise, particularly directional noise, which presents a new problem for HFSWR. Anisotropic directional noise has complex behavior, and its noise level is generally increased by 10 to 15 dB compared to traditional noise floor level. Suppressing varying directional noise and exploring obscured targets are challenging tasks for HFSWR. In this paper, a novel algorithm based on angle-Doppler joint multi-eigenvector synthesis, which considers the angle-Doppler map of radar echoes, is adopted to analyze the characteristics of the directional noise. Given the measured data set, we first analyze the directional noise-spatial correlation. Then, an algorithm based on sliding main lobe cancellation (SL-MLC) based on a sliding single-notch space filter (SSNSF) is proposed to block target components and get training data that contains precise directional noise information. Finally, the method is examined by measured data, and the results indicate the method has better performance for directional noise than the compared method. Full article
(This article belongs to the Special Issue Innovative Applications of HF Radar)
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20 pages, 51647 KiB  
Article
Antenna Pattern Calibration Method for Phased Array of High-Frequency Surface Wave Radar Based on First-Order Sea Clutter
by Hongbo Li, Aijun Liu, Qiang Yang, Changjun Yu and Zhe Lyv
Remote Sens. 2023, 15(24), 5789; https://doi.org/10.3390/rs15245789 - 18 Dec 2023
Cited by 2 | Viewed by 1495
Abstract
The problem of accurate source localization has been an area of focus in high-frequency surface wave radar (HFSWR) applications. However, antenna pattern distortion (APD) decreases the direction-of-arrival (DOA) estimation performance of the multiple signal classification (MUSIC) algorithm. Up to now, limited studies have [...] Read more.
The problem of accurate source localization has been an area of focus in high-frequency surface wave radar (HFSWR) applications. However, antenna pattern distortion (APD) decreases the direction-of-arrival (DOA) estimation performance of the multiple signal classification (MUSIC) algorithm. Up to now, limited studies have been conducted on the calibration of antenna pattern distortion for phased arrays in HFSWR. In this paper, we first analyze the effect of APD on the performance of the MUSIC algorithm through estimation of accuracy and angular resolution. We demonstrate that using the actual pattern (or say APD) can improve DOA estimation performance. Based on this proposition, we propose a novel iterative calibration method that employs the first-order sea clutter data and can jointly estimate DOA and APD in an iterative way. To obtain available calibration points, we introduce the extraction methods of the first-order sea clutter spectrum and single-DOA spectrum points. Meanwhile, in each iteration, the Beamspace MUSIC algorithm and artificial hummingbird algorithm (AHA) are utilized to estimate the DOA and APD, respectively. Numerical results reveal a good coincidence between the actual pattern and the estimated APD. We also apply this method to process the experimental data of HFSWR. We obtain the APD vector of the real phased array and improve the direction-finding performance of several real ship targets using this vector. Both numerical and experimental results prove the correctness of our proposed calibration method. Full article
(This article belongs to the Special Issue Innovative Applications of HF Radar)
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17 pages, 75484 KiB  
Article
Chaotic Properties of Gravity Waves during Typhoons Observed by HFSWR
by Xuekun Chen, Hongjuan Yang, Zhe Lyu and Changjun Yu
Remote Sens. 2023, 15(21), 5235; https://doi.org/10.3390/rs15215235 - 3 Nov 2023
Cited by 1 | Viewed by 872
Abstract
The gravity wave produced by typhoons has been an essential subject of study that concerns numerous researchers. The damage to human beings and infrastructure in coastal regions caused by typhoon disasters annually is very severe, and analyzing gravity wave variation is a reliable [...] Read more.
The gravity wave produced by typhoons has been an essential subject of study that concerns numerous researchers. The damage to human beings and infrastructure in coastal regions caused by typhoon disasters annually is very severe, and analyzing gravity wave variation is a reliable approach to research typhoons. High-frequency surface wave radar (HFSWR) as an over-the-horizon radar can achieve real-time monitoring of an extensive sea area and space. This paper derived the gravity wave perturbation spectrum by handling high-frequency surface wave radar data during typhoons. The gravity wave spectrum data were examined by applying the chaos examination approaches of the Lyapunov exponent and phase-space reconstruction to the gravity wave spectrum data after processing and extraction. The reconstructed phase space had a specific shape in a certain direction, with the maximum Lyapunov exponent greater than zero. The gravity wave spectrum data are suggested to have chaotic properties through two chaos examination approaches. This paper demonstrated that the gravity waves observed by a radar have chaotic properties through the measurement data of HFSWR. While the chaotic properties suggest that observed gravity wave data are predictable in the short term, they are unpredictable in the long term. Predicting gravity wave data is important for understanding the chaotic properties of the atmosphere and for future gravity wave prediction. Full article
(This article belongs to the Special Issue Innovative Applications of HF Radar)
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