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Airborne Distributed Radar Technology
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Airborne Distributed Radar Technology

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Radar Sensors".

Deadline for manuscript submissions: closed (20 March 2023) | Viewed by 10041

Special Issue Editors

Dr. Jianxin Wu

E-Mail Website
Guest Editor
School of Electronics and Communication Engineering, Sun Yat-Sen University, Guangzhou 510275, China
Interests: radar signal processing; array signal processing
Prof. Dr. Lei Zhang

E-Mail Website
Guest Editor
School of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou 510275, China
Interests: radar imaging; radar signal processing; distributed radar system
Special Issues, Collections and Topics in MDPI journals
Dr. Jingyue Lu

E-Mail Website
Guest Editor
National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
Interests: forward-looking airborne SAR imaging
Special Issues, Collections and Topics in MDPI journals
Dr. Yejian Zhou

E-Mail Website
Guest Editor
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
Interests: radar imaging; ISAR/SAR image interpretation; feature fusion

Special Issue Information

Dear Colleagues,

Airborne distributed radar is a new radar technology based on multiple radars, which improves the overall detection performance of the radar system through cooperative detection signal synthesis and information fusion. It involves many professional fields such as radar overall engineering, electromagnetic field and microwave, signal and information processing, as well as adaptive data processing and control. Distributed radars can effectively improve the anti-stealth, anti-interference, and survivability of radar systems. At the same time, it can also effectively solve the contradiction between radar powerful detection and high maneuverability, and has a very broad application prospect. Radar is one typical sensor used for sensing. As an airborne application, airborne distributed radar technology can be used for radar imaging, target detection, target tracking, and so on.

Dr. Jianxin Wu
Dr. Lei Zhang
Dr. Jingyue Lu
Dr. Yejian Zhou
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

  • airborne radar
  • distributed radar
  • radar signal processing
  • distributed array processing
  • target detection

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Published Papers (3 papers)

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Research

21 pages, 1681 KiB  
Article
Signal Property Information-Based Target Detection with Dual-Output Neural Network in Complex Environments
by Lu Shen, Hongtao Su, Zhi Mao, Xinchen Jing and Congyue Jia
Sensors 2023, 23(10), 4956; https://doi.org/10.3390/s23104956 - 22 May 2023
Cited by 1 | Viewed by 1473
Abstract
The performance of traditional model-based constant false-alarm ratio (CFAR) detection algorithms can suffer in complex environments, particularly in scenarios involving multiple targets (MT) and clutter edges (CE) due to an imprecise estimation of background noise power level. Furthermore, the fixed threshold mechanism that [...] Read more.
The performance of traditional model-based constant false-alarm ratio (CFAR) detection algorithms can suffer in complex environments, particularly in scenarios involving multiple targets (MT) and clutter edges (CE) due to an imprecise estimation of background noise power level. Furthermore, the fixed threshold mechanism that is commonly used in the single-input single-output neural network can result in performance degradation due to changes in the scene. To overcome these challenges and limitations, this paper proposes a novel approach, a single-input dual-output network detector (SIDOND) using data-driven deep neural networks (DNN). One output is used for signal property information (SPI)-based estimation of the detection sufficient statistic, while the other is utilized to establish a dynamic-intelligent threshold mechanism based on the threshold impact factor (TIF), where the TIF is a simplified description of the target and background environment information. Experimental results demonstrate that SIDOND is more robust and performs better than model-based and single-output network detectors. Moreover, the visual explanation technique is employed to explain the working of SIDOND. Full article
(This article belongs to the Special Issue Airborne Distributed Radar Technology)
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24 pages, 7508 KiB  
Article
Weak and Maneuvering Target Detection with Long Observation Time Based on Segment Fusion for Narrowband Radar
by Shaopeng Wei, Yan Dai and Qiang Zhang
Sensors 2022, 22(18), 7086; https://doi.org/10.3390/s22187086 - 19 Sep 2022
Cited by 2 | Viewed by 2532
Abstract
Detecting high-speed and maneuvering targets is challenging in early warning radar applications. Modern early warning radar has many functions such as detection, tracking, imaging, and recognition which need a high signal-to-noise ratio (SNR). Thus, long-time coherent integration is a necessary method to realize [...] Read more.
Detecting high-speed and maneuvering targets is challenging in early warning radar applications. Modern early warning radar has many functions such as detection, tracking, imaging, and recognition which need a high signal-to-noise ratio (SNR). Thus, long-time coherent integration is a necessary method to realize high SNR requirements. However, high-speed and maneuverable motion cause range and Doppler migration, which brings about serious coherent integration loss. Traditional integration methods usually have the drawbacks of model mismatching and high computational complexity. This paper establishes a novel long coherent processing interval (CPI) integration algorithm that detects maneuvering and weak targets which have a low reflection cross-section (RCS) and low echo SNR. The range and Doppler migration problems are solved via a layer integration by blending the association in a tracking-before-detection (TBD) technique. Compact SNR gain is achieved with a target information transmission mechanism and an updated constant false alarm ratio (CFAR) threshold. The algorithm is applicable in multiple target scenarios by considering different velocity ambiguities and maneuvers. A simulation and real-measured experiments confirm the effectiveness of the algorithm. Full article
(This article belongs to the Special Issue Airborne Distributed Radar Technology)
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24 pages, 13110 KiB  
Article
Radar Target Detection Algorithm Using Convolutional Neural Network to Process Graphically Expressed Range Time Series Signals
by Yan Dai, Dan Liu, Qingrong Hu and Xiaoli Yu
Sensors 2022, 22(18), 6868; https://doi.org/10.3390/s22186868 - 11 Sep 2022
Cited by 7 | Viewed by 5257
Abstract
Under the condition of low signal-to-noise ratio, the target detection performance of radar decreases, which seriously affects the tracking and recognition for the long-range small targets. To solve it, this paper proposes a target detection algorithm using convolutional neural network to process graphically [...] Read more.
Under the condition of low signal-to-noise ratio, the target detection performance of radar decreases, which seriously affects the tracking and recognition for the long-range small targets. To solve it, this paper proposes a target detection algorithm using convolutional neural network to process graphically expressed range time series signals. First, the two-dimensional echo signal was processed graphically. Second, the graphical echo signal was detected by the improved convolutional neural network. The simulation results under the condition of low signal-to-noise ratio show that, compared with the multi-pulse accumulation detection method, the detection method based on convolutional neural network proposed in this paper has a higher target detection probability, which reflects the effectiveness of the method proposed in this paper. Full article
(This article belongs to the Special Issue Airborne Distributed Radar Technology)
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