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Special Issue "Applications and New Trends in Metrology for Radar/LiDAR-Based Systems II"

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

Deadline for manuscript submissions: 31 October 2023 | Viewed by 7938

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Special Issue Editors

Prof. Dr. Silvia Liberata Ullo

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Department of Engineering (DING), University of Sannio, Benevento, Italy
Interests: signal processing; data and image analysis; satellite remote sensing; sensor networks; machine learning applied to satellite images
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Alfonso Farina

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Consultant, Rome, Italy
Interests: radar systems; adaptive beamforming; tracking algorithms; systems engineering
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yu Yao
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Guest Editor

School of Information Engineering, East China Jiaotong University, No. 808, E. Shuanggang Street, Nanchang 330013, China
Interests: statistical signal processing and optimization theory, with an emphasis on MIMO communications and radar signal processing

Prof. Dr. Harun Taha Hayvaci

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Guest Editor

College of Engineering and Technology, American University of the Middle East, Block 6, Building 1, Egaila, Kuwait
Interests: statistical signal processing; radar signal detection and estimation theory; statistical learning and classification; harmonic radar applications

Dr. Pia Addabbo
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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,

Following up the success of the previous Special Issue, a new one has been activated. The scope of this Special Issue is to provide an overview of methods and instruments for and practical experience with testing LiDAR and radar systems and subsystems (land-based, shipborne, and on board of drones, aircraft, and satellites) as well as to obtain measurements of environmental features through remote sensing applications. Specifically, topics of relevance to this Special Issue are: instrument test equipment for verification and validation in the industry, at the customer site, or in the field of operation; automation and remote test equipment; virtual reality technologies; both LiDAR and radar remote sensing applications.

Other topics relevant to this Special Issue are: the state-of-the-art radar system architectures and related digital and software technologies; cognitive radars and the analysis of human-in-the-loop aspects in radar systems; dual-function radar communications and radar systems; waveform design; radar detection theory and radar signal processing; theory, algorithms, and applications (RTAA); target classification; micromotion estimation.

We are collecting papers from university researchers, researchers in industries specialized in conceiving, designing, installing, operating, and testing radar systems for civilian and defense applications, users, and governmental/international agencies. We also invite authors from industries providing instruments to test radar performance to submit a paper on one or more of the above mentioned topics.

Prof. Dr. Silvia Liberata Ullo
Prof. Dr. Alfonso Farina
Prof. Dr. Yu Yao
Prof. Dr. Harun Taha Hayvaci
Prof. 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

radar and LiDAR systems
radar and LiDAR data measurements and processing
remote sensing applications
land-based, shipborne, and on board of drones, aircraft, and satellites
cognitive radars
human-in-the-loop aspects in radar systems
dual-function radar communications
waveform diversity and design
adaptive radar waveform design
radar detection theory
radar signal processing: theory, algorithms, and applications (RTAA)

Published Papers (6 papers)

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Research

Open AccessArticle

Airborne Streak Tube Imaging LiDAR Processing System: A Single Echo Fast Target Extraction Implementation

and

Remote Sens. 2023, 15(4), 1128; https://doi.org/10.3390/rs15041128 - 18 Feb 2023

Viewed by 855

Abstract

In this paper, a ground target extraction system for a novel LiDAR, airborne streak tube imaging LiDAR (ASTIL), is proposed. This system depends on only a single echo and a single data source, and can achieve fast ground target extraction. This system consists of two modules: Autofocus SSD (Single Shot MultiBox Detector) and post-processing. The Autofocus SSD proposed in this paper is used for object detection in the ASTIL echo signal, and its prediction speed exceeds that of the original SSD by a factor of three. In the post-processing module, we describe in detail how the echoes are processed into point clouds. The system was tested on a test set, and it can be seen from a visual perspective that satisfactory results were obtained for the extraction of buildings and trees. The system mAP^IoU=0.5 is 0.812, and the FPS is greater than 34. The results prove that this ASTIL processing system can achieve fast ground target extraction based on a single echo and a single data source. Full article

(This article belongs to the Special Issue Applications and New Trends in Metrology for Radar/LiDAR-Based Systems II)

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Open AccessArticle

UAV Propeller Rotational Speed Measurement through FMCW Radars

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Remote Sens. 2023, 15(1), 270; https://doi.org/10.3390/rs15010270 - 02 Jan 2023

Viewed by 1897

Abstract

The growing number of civil applications in which Unmanned Aerial Vehicles (UAVs) are involved can create many concerns for airspace security and surveillance. Gathering as much information as possible about a drone can be crucial to apply proper countermeasures if a potentially dangerous situation is detected. Of course, the presence of a UAV can be detected by radar, but it is possible to extend the system capabilities to obtain additional information. For example, in the case in which the UAV is equipped with propellers, the radar-measured rotational speed could be important information to classify the type of UAV or to reveal if it is carrying some possibly harmful payload. In addition, the rotational speed measured through radar could be used for different purposes, such as to detect a drone manumission, to estimate its maximum payload, or for predictive maintenance of the drone. Measuring the propellers’ rotational speed with radar systems is a critical task, as the Doppler generated by the rotation can be very high, and it is very difficult to find commercial radar systems in the market able to handle such a high Doppler. Another problem is caused by the typically very small Radar Cross-Section (RCS) of the propellers, which makes their detection even more difficult. In the literature, common detection techniques are based on the measurement of the Doppler effect produced by the propellers to derive their rotational speed, but due to the very limited capabilities of commercial sensors, this approach can be applied only at very low values of the rotational speed. In this work, a different approach based on a Frequency-Modulated Continuous Wave (FMCW) radar is proposed, which exploits the vibration of the UAV generated by the rotation of the propellers. The phenomenon and how the sensor can detect it will be presented, which is joined with a performance analysis comparing different estimation techniques for the indirect measurement of the propellers’ speed to evaluate the potential benefits of the proposed approach. Full article

(This article belongs to the Special Issue Applications and New Trends in Metrology for Radar/LiDAR-Based Systems II)

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Open AccessCommunication

Temporal Feature Learning and Pulse Prediction for Radars with Variable Parameters

Shuo Yuan

and

Zhang-Meng Liu

Remote Sens. 2022, 14(21), 5439; https://doi.org/10.3390/rs14215439 - 29 Oct 2022

Viewed by 829

Abstract

Many modern radars use variable pulse repetition intervals (PRI) to improve anti-reconnaissance and anti-jamming performance. Their PRI features are probably software-defined, but the PRI values at different time instants are variable. Previous statistical pattern analyzing methods are unable to extract such undetermined PRI values and features, which greatly increases the difficulty of Electronic Support Measures (ESM) against such radars. In this communication, we first establish a model to describe the temporal patterns of software-defined radar pulse trains, then introduce the recurrent neural network (RNN) to mine high-order relationships between successive pulses, and finally exploit the temporal features to predict the time of arrival of upcoming pulses. In the simulation part, we compare different time series prediction models to verify the RNN’s adaptability for pulse sequences of variable parameter radars. Moreover, behaviors of different RNN units in this task are compared, and the results show that the proposed method can learn complex PRI features in pulse trains even in the presence of significant data noises and agile PRIs. Full article

(This article belongs to the Special Issue Applications and New Trends in Metrology for Radar/LiDAR-Based Systems II)

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Open AccessArticle

Spatial Spectrum Estimation of Co-Channel Direct Signal in Passive Radar Based on Coprime Array

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Remote Sens. 2022, 14(21), 5308; https://doi.org/10.3390/rs14215308 - 24 Oct 2022

Cited by 1 | Viewed by 843

Abstract

The received signal of passive radar based on mobile communication signals contains direct and multipath interference (DMI) signals from multiple co-channel base stations (CCBS). The direct signal spatial spectrum of each CCBS should be obtained first to eliminate the co-channel interference. The performance of the traditional spatial spectrum estimation algorithms based on uniform linear array (ULA) is related to the number of array elements. In the complex co-channel interference environment, the array requires an ultra-large number of array elements and the spatial spectrum estimation resolution is poor. This paper proposes a method for estimating the direct signal spatial spectrum of the CCBS by fusing coprime array and compressive sensing. Firstly, an augmented virtual array signal is constructed by using the second-order statistics of the received signals of the coprime array and then the compressive sensing algorithm is used to estimate the spatial spectrum of the direct signal of the CCBS. The proposed method can achieve higher resolution and higher-degrees-of-freedom (DOFs) performance than traditional ULA by using the same number of array elements. The effectiveness of the proposed method is verified by numerical simulation analysis and experimental data. Full article

(This article belongs to the Special Issue Applications and New Trends in Metrology for Radar/LiDAR-Based Systems II)

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Open AccessArticle

Reflective Tomography Lidar Image Reconstruction for Long Distance Non-Cooperative Target

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Remote Sens. 2022, 14(14), 3310; https://doi.org/10.3390/rs14143310 - 09 Jul 2022

Cited by 2 | Viewed by 1092

Abstract

In the long-distance space target detection, the technique of laser reflection tomography (LRT) shows great power and attracts more attention for further study and real use. However, space targets are often non-cooperative, and normally a 360° complete view of reflection projections cannot be obtained. Therefore, this article firstly introduces an improved LRT system design with more advanced laser equipment for long-distance non-cooperative detection to ensure the high quality of the lidar beam and the lidar projection data. Then, the LRT image reconstruction is proposed and focused on the laser image reconstruction method utilizing the total variation (TV) minimization approach based on the sparse algebraic reconstruction technique (ART) model, in order to reconstruct the laser image in a sparse or incomplete view of projections. At last, comparative experiments with the system are performed to validate the advantages of this method with the LRT system. In both near and far field experiments, the effectiveness and superiority of the proposed method are verified for different types of projection data through comparison to typical methods. Full article

(This article belongs to the Special Issue Applications and New Trends in Metrology for Radar/LiDAR-Based Systems II)

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Open AccessArticle

Joint Antenna Placement and Power Allocation for Target Detection in a Distributed MIMO Radar Network

Cheng Qi

Junwei Xie

and

Haowei Zhang

Remote Sens. 2022, 14(11), 2650; https://doi.org/10.3390/rs14112650 - 01 Jun 2022

Cited by 4 | Viewed by 1330

Abstract

Radar network configuration and power allocation are of great importance in military applications, where the entire surveillance area needs to be searched under resource budget constraints. To pursue the joint antenna placement and power allocation (JAPPA) optimization, this paper develops a JAPPA strategy to improve target detection performance in a widely distributed multiple-input and multiple-output (MIMO) radar network. First, the three variables of the problem are incorporated into the Neyman–Pearson (NP) detector by using the antenna placement optimization and the Lagrange power allocation method. Further, an improved iterative greedy dropping heuristic method based on a two-stage local search is proposed to solve the NP-hard issues of high-dimensional non-linear integer programming. Then, the sum of the weighted logarithmic likelihood ratio test (LRT) function is constructed as optimization criteria for the JAPPA approach. Numerical simulations and the theoretical analysis confirm the superiority of the proposed algorithm in terms of achieving effective overall detection performance. Full article

(This article belongs to the Special Issue Applications and New Trends in Metrology for Radar/LiDAR-Based Systems II)

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