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Radar Technology and Data Processing

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 19159

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Special Issue Editors

Prof. Dr. Adam M. Kawalec

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Guest Editor

Faculty of Mechatronics, Armaments and Aerospace, Military University of Technology, 00-908 Warsaw, Poland
Interests: digital signal processing; radar design and radar signal processing
Special Issues, Collections and Topics in MDPI journals

Dr. Marta Walenczykowska

E-Mail Website1 Website2
Guest Editor

Faculty of Mechatronics, Armament and Aerospace, Military University of Technology, 00-908 Warsaw, Poland
Interests: digital signal processing; signal recognition and classification algorithms; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Ksawery Krenc

E-Mail Website
Guest Editor

Faculty of Mechatronics, Armament and Aerospace, Military University of Technology, 00-908 Warsaw, Poland
Interests: data and information fusion; tracking manoeuvring objects; C4I systems

Special Issue Information

Dear Colleagues,

Radar technology has been known for more than 100 years and it has been under permanent development for both civilian and military applications. The development of radar technology has been associated with data processing, and both have evolved together. The Special Issue aims at papers presenting the progress in fields like system architecture, waveforms, radar sensors, artificial intelligence, signal recognition and classification, data and information fusion, target tracking, device design, new applications, and practical solutions. Moreover, there is always a need to find a fresh angle on radar technology and data processing problems to identify innovative solutions in this area.

Therefore, it is our pleasure to invite you all to contribute to this Special Issue of Sensors, the topics of interest for which include, but are not limited to, the following:

Radar sensors design and platform developments
Antenna design, modeling, and measurements
Waveform design techniques
Radar signal processing
Algorithms for real-time radar signal processing
Radar signal recognition and classification algorithms
Radar sensors in robotics
Active and passive devices
Multi-sensor data fusion
Radar target tracking
Artificial Intelligence for Radar Technology and Data Processing
Multi-level Information Fusion

Prof. Dr. Adam M. Kawalec
Dr. Marta Walenczykowska
Dr. Ksawery Krenc
Guest Editors

Manuscript Submission Information

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Keywords

radar signal processing techniques
data processing problems
multi-level information fusion
artificial intelligence and machine learning for radar
radar sensors for robots
radar imaging
modern radar applications

Published Papers (11 papers)

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Research

24 pages, 10706 KiB

Open AccessArticle

Adaptive Point-Line Fusion: A Targetless LiDAR–Camera Calibration Method with Scheme Selection for Autonomous Driving

by Yingtong Zhou, Tiansi Han, Qiong Nie, Yuxuan Zhu, Minghu Li, Ning Bian and Zhiheng Li

Sensors 2024, 24(4), 1127; https://0-doi-org.brum.beds.ac.uk/10.3390/s24041127 - 08 Feb 2024

Viewed by 665

Abstract

Accurate calibration between LiDAR and camera sensors is crucial for autonomous driving systems to perceive and understand the environment effectively. Typically, LiDAR–camera extrinsic calibration requires feature alignment and overlapping fields of view. Aligning features from different modalities can be challenging due to noise influence. Therefore, this paper proposes a targetless extrinsic calibration method for monocular cameras and LiDAR sensors that have a non-overlapping field of view. The proposed solution uses pose transformation to establish data association across different modalities. This conversion turns the calibration problem into an optimization problem within a visual SLAM system without requiring overlapping views. To improve performance, line features serve as constraints in visual SLAM. Accurate positions of line segments are obtained by utilizing an extended photometric error optimization method. Moreover, a strategy is proposed for selecting appropriate calibration methods from among several alternative optimization schemes. This adaptive calibration method selection strategy ensures robust calibration performance in urban autonomous driving scenarios with varying lighting and environmental textures while avoiding failures and excessive bias that may result from relying on a single approach. Full article

(This article belongs to the Special Issue Radar Technology and Data Processing)

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13 pages, 7271 KiB

Open AccessArticle

Microwave Absolute Distance Measurement Method with Ten-Micron-Level Accuracy and Meter-Level Range Based on Frequency Domain Interferometry

by Longhuang Tang, Xing Jia, Heli Ma, Shenggang Liu, Yongchao Chen, Tianjiong Tao, Long Chen, Jian Wu, Chengjun Li, Xiang Wang and Jidong Weng

Sensors 2023, 23(18), 7898; https://0-doi-org.brum.beds.ac.uk/10.3390/s23187898 - 15 Sep 2023

Viewed by 969

Abstract

A microwave absolute distance measurement method with ten-micron-level accuracy and meter-level range based on frequency domain interferometry is proposed and experimentally demonstrated for the first time. Theoretical analysis indicates that an interference phenomenon occurs instantaneously in the frequency domain when combining two homologous broad-spectrum microwave beams with different paths, and the absolute value of the distance difference between the two paths is only inversely proportional to the period of frequency domain interference fringes. The proof-of-principle experiments were performed to prove that the proposed method can achieve absolute distance measurement in the X-band with standard deviations of 15 μm, 17 μm, and 26 μm and within ranges of 1.69 m, 2.69 m, and 3.75 m. Additionally, a displacement resolution of 100 microns was realized. The multi-target recognition performance was also verified in principle. Furthermore, at the expense of a slight decrease in ranging accuracy, a fast distance measurement with the single measurement time of 20 μs was achieved by using a digitizer combined with a Fourier transform analyzer. Compared with the current microwave precision ranging technologies, the proposed method not only has the advantages of high precision, large range, and rapid measurement capability, but the required components are also easily obtainable commercial devices. The proposed method also has better complex engineering applicability, because the ten-micron-level ranging accuracy is achievable only by using a simple Fourier transform without any phase estimation algorithm, which greatly reduces the requirement for signal-to-noise ratio. Full article

(This article belongs to the Special Issue Radar Technology and Data Processing)

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18 pages, 16066 KiB

Open AccessArticle

Dynamic Electromagnetic Scattering Simulation of Tilt-Rotor Aircraft in Multiple Modes

by Zhongyang Fei, Yan Yang, Xiangwen Jiang, Qijun Zhao and Xi Chen

Sensors 2023, 23(17), 7606; https://0-doi-org.brum.beds.ac.uk/10.3390/s23177606 - 01 Sep 2023

Viewed by 886

Abstract

To study the electromagnetic scattering of tilt-rotor aircraft during multi-mode continuous flight, a dynamic simulation approach is presented. A time-varying mesh method is established to characterize the dynamic rotation and tilting of tilt-rotor aircraft. Shooting and bouncing rays and the uniform theory of diffraction are used to calculate the multi-mode radar cross-section (RCS). And the scattering mechanisms of tilt-rotor aircraft are investigated by extracting the micro-Doppler and inverse synthetic aperture radar images. The results show that the dynamic RCS of tilt-rotor aircraft in helicopter and airplane mode exhibits obvious periodicity, and the transition mode leads to a strong specular reflection on the rotor’s upper surface, which increases the RCS with a maximum increase of about 36 dB. The maximum micro-Doppler shift has functional relationships with flight time, tilt speed, and wave incident direction. By analyzing the change patterns of maximum shift, the real-time flight state and mode can be identified. There are some significant scattering sources on the body of tilt-rotor aircraft that are distributed in a planar or point-like manner, and the importance of different scattering sources varies in different flight modes. The pre-studies on the key scattering areas can provide effective help for the stealth design of the target. Full article

(This article belongs to the Special Issue Radar Technology and Data Processing)

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20 pages, 11514 KiB

Open AccessArticle

Machine Learning-Based Human Posture Identification from Point Cloud Data Acquisitioned by FMCW Millimetre-Wave Radar

by Guangcheng Zhang, Shenchen Li, Kai Zhang and Yueh-Jaw Lin

Sensors 2023, 23(16), 7208; https://0-doi-org.brum.beds.ac.uk/10.3390/s23167208 - 16 Aug 2023

Cited by 1 | Viewed by 1503

Abstract

Human posture recognition technology is widely used in the fields of healthcare, human-computer interaction, and sports. The use of a Frequency-Modulated Continuous Wave (FMCW) millimetre-wave (MMW) radar sensor in measuring human posture characteristics data is of great significance because of its robust and strong recognition capabilities. This paper demonstrates how human posture characteristics data are measured, classified, and identified using FMCW techniques. First of all, the characteristics data of human posture is measured with the MMW radar sensors. Secondly, the point cloud data for human posture is generated, considering both the dynamic and static features of the reflected signal from the human body, which not only greatly reduces the environmental noise but also strengthens the reflection of the detected target. Lastly, six different machine learning models are applied for posture classification based on the generated point cloud data. To comparatively evaluate the proper model for point cloud data classification procedure—in addition to using the traditional index—the Kappa index was introduced to eliminate the effect due to the uncontrollable imbalance of the sampling data. These results support our conclusion that among the six machine learning algorithms implemented in this paper, the multi-layer perceptron (MLP) method is regarded as the most promising classifier. Full article

(This article belongs to the Special Issue Radar Technology and Data Processing)

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17 pages, 1447 KiB

Open AccessArticle

Change Point Detection for Fine-Grained MFR Work Modes with Multi-Head Attention-Based Bi-LSTM Network

by Yiying Fang, Qihang Zhai, Ziwei Zhang and Jing Yang

Sensors 2023, 23(6), 3326; https://0-doi-org.brum.beds.ac.uk/10.3390/s23063326 - 22 Mar 2023

Cited by 1 | Viewed by 1367

Abstract

Detection of the changes in Multi-Functional Radar (MFR) work modes is a critical situation assessment task for Electronic Support Measure (ESM) systems. There are two major challenges that must be addressed: (i) The received radar pulse stream may contain multiple work mode segments of unknown number and duration, which makes the Change Point Detection (CPD) difficult. (ii) Modern MFRs can produce a variety of parameter-level (fine-grained) work modes with complex and flexible patterns, which are challenging to detect through traditional statistical methods and basic learning models. To address the challenges, a deep learning framework is proposed for fine-grained work mode CPD in this paper. First, the fine-grained MFR work mode model is established. Then, a multi-head attention-based bi-directional long short-term memory network is introduced to abstract high-order relationships between successive pulses. Finally, temporal features are adopted to predict the probability of each pulse being a change point. The framework further improves the label configuration and the loss function of training to mitigate the label sparsity problem effectively. The simulation results showed that compared with existing methods, the proposed framework effectively improves the CPD performance at parameter-level. Moreover, the F1-score was increased by 4.15% under hybrid non-ideal conditions. Full article

(This article belongs to the Special Issue Radar Technology and Data Processing)

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15 pages, 7990 KiB

Open AccessArticle

Driving Activity Recognition Using UWB Radar and Deep Neural Networks

by Iuliia Brishtel, Stephan Krauss, Mahdi Chamseddine, Jason Raphael Rambach and Didier Stricker

Sensors 2023, 23(2), 818; https://0-doi-org.brum.beds.ac.uk/10.3390/s23020818 - 10 Jan 2023

Cited by 11 | Viewed by 3161

Abstract

In-car activity monitoring is a key enabler of various automotive safety functions. Existing approaches are largely based on vision systems. Radar, however, can provide a low-cost, privacy-preserving alternative. To this day, such systems based on the radar are not widely researched. In our work, we introduce a novel approach that uses the Doppler signal of an ultra-wideband (UWB) radar as an input to deep neural networks for the classification of driving activities. In contrast to previous work in the domain, we focus on generalization to unseen persons and make a new radar driving activity dataset (RaDA) available to the scientific community to encourage comparison and the benchmarking of future methods. Full article

(This article belongs to the Special Issue Radar Technology and Data Processing)

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16 pages, 6210 KiB

Open AccessArticle

Exploration of Effective Time-Velocity Distribution for Doppler-Radar-Based Personal Gait Identification Using Deep Learning

by Keitaro Shioiri and Kenshi Saho

Sensors 2023, 23(2), 604; https://0-doi-org.brum.beds.ac.uk/10.3390/s23020604 - 05 Jan 2023

Viewed by 1400

Abstract

Personal identification based on radar gait measurement is an important application of biometric technology because it enables remote and continuous identification of people, irrespective of the lighting conditions and subjects’ outfits. This study explores an effective time-velocity distribution and its relevant parameters for Doppler-radar-based personal gait identification using deep learning. Most conventional studies on radar-based gait identification used a short-time Fourier transform (STFT), which is a general method to obtain time-velocity distribution for motion recognition using Doppler radar. However, the length of the window function that controls the time and velocity resolutions of the time-velocity image was empirically selected, and several other methods for calculating high-resolution time-velocity distributions were not considered. In this study, we compared four types of representative time-velocity distributions calculated from the Doppler-radar-received signals: STFT, wavelet transform, Wigner–Ville distribution, and smoothed pseudo-Wigner–Ville distribution. In addition, the identification accuracies of various parameter settings were also investigated. We observed that the optimally tuned STFT outperformed other high-resolution distributions, and a short length of the window function in the STFT process led to a reasonable accuracy; the best identification accuracy was 99% for the identification of twenty-five test subjects. These results indicate that STFT is the optimal time-velocity distribution for gait-based personal identification using the Doppler radar, although the time and velocity resolutions of the other methods were better than those of the STFT. Full article

(This article belongs to the Special Issue Radar Technology and Data Processing)

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21 pages, 6988 KiB

Open AccessArticle

Azimuth Full-Aperture Processing of Spaceborne Squint SAR Data with Block Varying PRF

by Zhuo Zhang, Wei Xu, Pingping Huang, Weixian Tan, Zhiqi Gao and Yaolong Qi

Sensors 2022, 22(23), 9328; https://0-doi-org.brum.beds.ac.uk/10.3390/s22239328 - 30 Nov 2022

Cited by 4 | Viewed by 1554

Abstract

The block varying pulse repetition frequency (BV-PRF) scheme applied to spaceborne squint sliding-spotlight synthetic aperture radar (SAR) can resolve large-range cell migration (RCM) and reduce azimuth signal non-uniformity. However, in the BV-PRF scheme, different raw data blocks have different PRFs, and the raw data in each block are insufficiently sampled. To resolve the two problems, a novel azimuth full-aperture pre-processing method is proposed to handle the SAR raw data formed by the BV-PRF scheme. The key point of the approach is the resampling of block data with different PRFs and the continuous splicing of azimuth data. The method mainly consists of four parts: de-skewing, resampling, azimuth continuous combination, and Doppler history recovery. After de-skewing, the raw data with different PRFs can be resampled individually to obtain a uniform azimuth sampling interval, and an appropriate azimuth time shift is introduced to ensure the continuous combination of the azimuth signal. Consequently, the resulting raw data are sufficiently and uniformly sampled in azimuth, which could be well handled by classical SAR-focusing algorithms. Simulation results on point targets validate the proposed azimuth pre-processing approach. Furthermore, compared with methods to process SAR data with continuous PRF, the proposed method is more effective. Full article

(This article belongs to the Special Issue Radar Technology and Data Processing)

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20 pages, 3250 KiB

Open AccessArticle

An Image Fusion Method of SAR and Multispectral Images Based on Non-Subsampled Shearlet Transform and Activity Measure

by Dengshan Huang, Yulin Tang and Qisheng Wang

Sensors 2022, 22(18), 7055; https://0-doi-org.brum.beds.ac.uk/10.3390/s22187055 - 18 Sep 2022

Cited by 5 | Viewed by 1472

Abstract

Synthetic aperture radar (SAR) is an important remote sensing sensor whose application is becoming more and more extensive. Compared with traditional optical sensors, it is not easy to be disturbed by the external environment and has a strong penetration. Limited by its working principles, SAR images are not easily interpreted, and fusing SAR images with optical multispectral images is a good solution to improve the interpretability of SAR images. This paper presents a novel image fusion method based on non-subsampled shearlet transform and activity measure to fuse SAR images with multispectral images, whose aim is to improve the interpretation ability of SAR images easily obtained at any time, rather than producing a fused image containing more information, which is the pursuit of previous fusion methods. Three different sensors, together with different working frequencies, polarization modes and spatial resolution SAR datasets, are used to evaluate the proposed method. Both visual evaluation and statistical analysis are performed, the results show that satisfactory fusion results are achieved through the proposed method and the interpretation ability of SAR images is effectively improved compared with the previous methods. Full article

(This article belongs to the Special Issue Radar Technology and Data Processing)

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12 pages, 12542 KiB

Open AccessArticle

Non-Ionizing Radiation Measurements for Trajectography Radars

by J. Marcos Leal Barbosa Filho, Millena M. de M. Campos, Daniel L. Flor, William S. Alves, Adaildo G. D’Assunção, Marcio E. C. Rodrigues and Vicente A. de Sousa, Jr.

Sensors 2022, 22(18), 7017; https://0-doi-org.brum.beds.ac.uk/10.3390/s22187017 - 16 Sep 2022

Viewed by 1998

Abstract

This work presents a Non-Ionizing Radiation (NIR) measurement campaign and proposes a specific measurement method for trajectography radars. This kind of radar has a high gain narrow beam antenna and emits a high power signal. Power density measurements from a C-band trajectography radar are carried out using bench equipment and a directional receiving antenna, instead of the commonly used isotropic probe. The measured power density levels are assessed for compliance test via comparison with the occupational and general public exposure limit levels of both the International Commission on Non-Ionizing Radiation Protection (ICNIRP) and the Brazilian National Telecommunication Agency (Anatel). The limit for the occupational public is respected everywhere, evidencing the safe operation of the studied radar. However, the limit for the general public is exceeded at a point next to the radar’s antenna, showing that preventive measures are needed. Full article

(This article belongs to the Special Issue Radar Technology and Data Processing)

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21 pages, 1560 KiB

Open AccessArticle

Application of Radar Solutions for the Purpose of Bird Tracking Systems Based on Video Observation

by Ksawery Krenc, Dawid Gradolewski, Damian Dziak and Adam Kawalec

Sensors 2022, 22(10), 3660; https://0-doi-org.brum.beds.ac.uk/10.3390/s22103660 - 11 May 2022

Cited by 2 | Viewed by 1900

Abstract

Wildlife Hazard Management is nowadays a very serious problem, mostly at airports and wind farms. If ignored, it may lead to repercussions in human safety, ecology, and economics. One of the approaches that is widely implemented in small and medium-size airports, as well as on wind turbines is based on a stereo-vision. However, to provide long-term observations allowing the determination of the hot spots of birds’ activity and forecast future events, a robust tracking algorithm is required. The aim of this paper is to review tracking algorithms widely used in Radar Science and assess the possibilities of application of these algorithms for the purpose of tracking birds with a stereo-vision system. We performed a survey-of-related works and simulations determined five state-of-the art algorithms: Kalman Filter, Nearest-Neighbour, Joint-Probabilistic Data Association, and Interacting Multiple Model with the potential for implementation in a stereo-vision system. These algorithms have been implemented and simulated in the proposed case study Full article

(This article belongs to the Special Issue Radar Technology and Data Processing)

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