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

Deadline for manuscript submissions: closed (10 September 2024) | Viewed by 41620

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Prof. Dr. Janusz Dudczyk

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Institute of Communications Systems, Faculty of Electronics, Military University of Technology, 00-908 Warsaw, Poland
Interests: specific emitter identification; radar signal processing; feature extraction; radar emitter recognition; radar emitter identification; C4I Systems; ELINT systems; electronic warfare systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Piotr Samczyński

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Faculty of Electronics and Information Technology, Institute of Electronic Systems, Warsaw University of Technology, 00-661 Warsaw, Poland
Interests: SAR/ISAR; passive radars; passive SAR/ISAR; noise radars; radar signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the theory of radar signal recognition and identification, the main objective is to identify distinctive patterns of such signals and establish methods for distinguishing them. In the literature, source emission pattern-separating surfaces in the measurable feature space is one such widely used term to describe pattern recognition and classification. Because radar emitter recognition and classification is based on defining the location of an emission source from said separating surfaces, it is essential to indicate a very significant fact: radar metrics need to be defined in the measurable feature space of a signal, and the specific features of a radar signal must be extracted in order to establish a distinctive radar signal pattern.

Recently, rapid development has taken place in electronic warfare systems. Different methods for the observation of electromagnetic environments are used to analyze radar signatures. These methods increase the quality of algorithms that recognize objects and targets automatically. One such difference lies in the procedures for gaining distinctive features.

The aim of this Special Issue is to present the latest modern solutions in radar signal recognition and identification, utilizing measurement and signature intelligence and extracting distinctive features from radar signals in different applications, including both military use and a broad spectrum of civilian applications. Contributions from leading experts in this field of research will be collected and presented in this Special Issue.

The 11th Microwave and Radar Week (MRW-2024) will take place in Wroclaw between 1st and 4th of July 2024. The authors of selected high-quality papers that fit the scope of Sensors from the conference will be invited to submit extended versions of their original papers (50% extension of the contents of the conference paper) to this Special Issue.

This Special Issue aims to highlight advances in radar signal recognition and identification. Topics of interest include, but are not limited to, the following:

Modern solutions in radar signal detection, recognition and identification;
The identification of specific radar emitters;
New technologies in the process of feature extraction from radar signals;
Classification methods and the data particle divide;
New technologies in radar signal and data processing;
Applications of artificial intelligence in radar signal detection, recognition and identification;
Countermeasures to modern radar and ELINT systems;
Civilian and military applications of modern radar technology in electronic warfare.

Prof. Dr. Janusz Dudczyk
Prof. Dr. Piotr Samczyński
Guest Editors

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Keywords

radar signal detection
recognition
identification

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Related Special Issue

Detection, Recognition and Identification in the Radar Applications in Sensors

Published Papers (12 papers)

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Research

17 pages, 5247 KiB

Open AccessArticle

Intra-Pulse Modulation Recognition of Radar Signals Based on Efficient Cross-Scale Aware Network

by Jingyue Liang, Zhongtao Luo and Renlong Liao

Sensors 2024, 24(16), 5344; https://doi.org/10.3390/s24165344 - 18 Aug 2024

Cited by 1 | Viewed by 1782

Abstract

Radar signal intra-pulse modulation recognition can be addressed with convolutional neural networks (CNNs) and time–frequency images (TFIs). However, current CNNs have high computational complexity and do not perform well in low-signal-to-noise ratio (SNR) scenarios. In this paper, we propose a lightweight CNN known as the cross-scale aware network (CSANet) to recognize intra-pulse modulation based on three types of TFIs. The cross-scale aware (CSA) module, designed as a residual and parallel architecture, comprises a depthwise dilated convolution group (DDConv Group), a cross-channel interaction (CCI) mechanism, and spatial information focus (SIF). DDConv Group produces multiple-scale features with a dynamic receptive field, CCI fuses the features and mitigates noise in multiple channels, and SIF is aware of the cross-scale details of TFI structures. Furthermore, we develop a novel time–frequency fusion (TFF) feature based on three types of TFIs by employing image preprocessing techniques, i.e., adaptive binarization, morphological processing, and feature fusion. Experiments demonstrate that CSANet achieves higher accuracy with our TFF compared to other TFIs. Meanwhile, CSANet outperforms cutting-edge networks across twelve radar signal datasets, providing an efficient solution for high-precision recognition in low-SNR scenarios. Full article

(This article belongs to the Special Issue Radar Signal Detection, Recognition and Identification)

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25 pages, 2214 KiB

Open AccessArticle

On a Closer Look of a Doppler Tolerant Noise Radar Waveform in Surveillance Applications

by Maximiliano Barbosa, Leandro Pralon, Antonio L. L. Ramos and José Antonio Apolinário, Jr.

Sensors 2024, 24(8), 2532; https://doi.org/10.3390/s24082532 - 15 Apr 2024

Cited by 1 | Viewed by 1967

Abstract

The prevalence of Low Probability of Interception (LPI) and Low Probability of Exploitation (LPE) radars in contemporary Electronic Warfare (EW) presents an ongoing challenge to defense mechanisms, compelling constant advances in protective strategies. Noise radars are examples of LPI and LPE systems that gained substantial prominence in the past decade despite exhibiting a common drawback of limited Doppler tolerance. The Advanced Pulse Compression Noise (APCN) waveform is a stochastic radar signal proposed to amalgamate the LPI and LPE attributes of a random waveform with the Doppler tolerance feature inherent to a linear frequency modulation. In the present work, we derive closed-form expressions describing the APCN signal’s ambiguity function and spectral containment that allow for a proper analysis of its detection performance and ability to remove range ambiguities as a function of its stochastic parameters. This paper also presents a more detailed address of the LPI/LPE characteristic of APCN signals claimed in previous works. We show that sophisticated Electronic Intelligence (ELINT) systems that employ Time Frequency Analysis (TFA) and image processing methods may intercept APCN and estimate important parameters of APCN waveforms, such as bandwidth, operating frequency, time duration, and pulse repetition interval. We also present a method designed to intercept and exploit the unique characteristics of the APCN waveform. Its performance is evaluated based on the probability of such an ELINT system detecting an APCN radar signal as a function of the Signal-to-Noise Ratio (SNR) in the ELINT system. We evaluated the accuracy and precision of the random variables characterizing the proposed estimators as a function of the SNR. Results indicate a probability of detection close to 1 and show good performance, even for scenarios with a SNR slightly less than

- 10

dB. The contributions in this work offer enhancements to noise radar capabilities while facilitating improvements in ESM systems. Full article

(This article belongs to the Special Issue Radar Signal Detection, Recognition and Identification)

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20 pages, 8200 KiB

Open AccessArticle

Efficient FPGA Implementation of Convolutional Neural Networks and Long Short-Term Memory for Radar Emitter Signal Recognition

by Bin Wu, Xinyu Wu, Peng Li, Youbing Gao, Jiangbo Si and Naofal Al-Dhahir

Sensors 2024, 24(3), 889; https://doi.org/10.3390/s24030889 - 30 Jan 2024

Cited by 9 | Viewed by 3057

Abstract

In recent years, radar emitter signal recognition has enjoyed a wide range of applications in electronic support measure systems and communication security. More and more deep learning algorithms have been used to improve the recognition accuracy of radar emitter signals. However, complex deep learning algorithms and data preprocessing operations have a huge demand for computing power, which cannot meet the requirements of low power consumption and high real-time processing scenarios. Therefore, many research works have remained in the experimental stage and cannot be actually implemented. To tackle this problem, this paper proposes a resource reuse computing acceleration platform based on field programmable gate arrays (FPGA), and implements a one-dimensional (1D) convolutional neural network (CNN) and long short-term memory (LSTM) neural network (NN) model for radar emitter signal recognition, directly targeting the intermediate frequency (IF) data of radar emitter signal for classification and recognition. The implementation of the 1D-CNN-LSTM neural network on FPGA is realized by multiplexing the same systolic array to accomplish the parallel acceleration of 1D convolution and matrix vector multiplication operations. We implemented our network on Xilinx XCKU040 to evaluate the effectiveness of our proposed solution. Our experiments show that the system can achieve 7.34 giga operations per second (GOPS) data throughput with only 5.022 W power consumption when the radar emitter signal recognition rate is 96.53%, which greatly improves the energy efficiency ratio and real-time performance of the radar emitter recognition system. Full article

(This article belongs to the Special Issue Radar Signal Detection, Recognition and Identification)

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18 pages, 2220 KiB

Open AccessArticle

An Application of Analytic Wavelet Transform and Convolutional Neural Network for Radar Intrapulse Modulation Recognition

by Marta Walenczykowska, Adam Kawalec and Ksawery Krenc

Sensors 2023, 23(4), 1986; https://doi.org/10.3390/s23041986 - 10 Feb 2023

Cited by 11 | Viewed by 2955

Abstract

This article analyses the possibility of using the Analytic Wavelet Transform (AWT) and the Convolutional Neural Network (CNN) for the purpose of recognizing the intrapulse modulation of radar signals. Firstly, the possibilities of using AWT by the algorithms of automatic signal recognition are discussed. Then, the research focuses on the influence of the parameters of the generalized Morse wavelet on the classification accuracy. The paper’s novelty is also related to the use of the generalized Morse wavelet (GMW) as a superfamily of analytical wavelets with a Convolutional Neural Network (CNN) as classifier applied for intrapulse recognition purposes. GWT is used to obtain time–frequency images (TFI), and SqueezeNet was chosen as the CNN classifier. The article takes into account selected types of intrapulse modulation, namely linear frequency modulation (LFM) and the following types of phase-coded waveform (PCW): Frank, Barker, P1, P2, and Px. The authors also consider the possibility of using other time–frequency transformations such as Short-Time Fourier Transform(STFT) or Wigner–Ville Distribution (WVD). Finally, authors present the results of the simulation tests carried out in the Matlab environment, taking into account the signal-to-noise ratio (SNR) in the range from −6 to 0 dB. Full article

(This article belongs to the Special Issue Radar Signal Detection, Recognition and Identification)

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18 pages, 4816 KiB

Open AccessArticle

Low Complexity Radar Gesture Recognition Using Synthetic Training Data

by Yanhua Zhao, Vladica Sark, Milos Krstic and Eckhard Grass

Sensors 2023, 23(1), 308; https://doi.org/10.3390/s23010308 - 28 Dec 2022

Cited by 16 | Viewed by 3852

Abstract

Developments in radio detection and ranging (radar) technology have made hand gesture recognition feasible. In heat map-based gesture recognition, feature images have a large size and require complex neural networks to extract information. Machine learning methods typically require large amounts of data and collecting hand gestures with radar is time- and energy-consuming. Therefore, a low computational complexity algorithm for hand gesture recognition based on a frequency-modulated continuous-wave (FMCW) radar and a synthetic hand gesture feature generator are proposed. In the low computational complexity algorithm, two-dimensional Fast Fourier Transform is implemented on the radar raw data to generate a range-Doppler matrix. After that, background modelling is applied to separate the dynamic object and the static background. Then a bin with the highest magnitude in the range-Doppler matrix is selected to locate the target and obtain its range and velocity. The bins at this location along the dimension of the antenna can be utilised to calculate the angle of the target using Fourier beam steering. In the synthetic generator, the Blender software is used to generate different hand gestures and trajectories and then the range, velocity and angle of targets are extracted directly from the trajectory. The experimental results demonstrate that the average recognition accuracy of the model on the test set can reach 89.13% when the synthetic data are used as the training set and the real data are used as the test set. This indicates that the generation of synthetic data can make a meaningful contribution in the pre-training phase. Full article

(This article belongs to the Special Issue Radar Signal Detection, Recognition and Identification)

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18 pages, 5512 KiB

Open AccessArticle

Recognition of Targets in SAR Images Based on a WVV Feature Using a Subset of Scattering Centers

by Sumi Lee and Sang-Wan Kim

Sensors 2022, 22(21), 8528; https://doi.org/10.3390/s22218528 - 5 Nov 2022

Cited by 4 | Viewed by 2144

Abstract

This paper proposes a robust method for feature-based matching with potential for application to synthetic aperture radar (SAR) automatic target recognition (ATR). The scarcity of measured SAR data available for training classification algorithms leads to the replacement of such data with synthetic data. As attributed scattering centers (ASCs) extracted from the SAR image reflect the electromagnetic phenomenon of the SAR target, this is effective for classifying targets when purely synthetic SAR images are used as the template. In the classification stage, following preparation of the extracted template ASC dataset, some of the template ASCs were subsampled by the amplitude and the neighbor matching algorithm to focus on the related points of the test ASCs. Then, the subset of ASCs were reconstructed to the world view vector feature set, considering the point similarity and structure similarity simultaneously. Finally, the matching scores between the two sets were calculated using weighted bipartite graph matching and then combined with several weights for overall similarity. Experiments on synthetic and measured paired labeled experiment datasets, which are publicly available, were conducted to verify the effectiveness and robustness of the proposed method. The proposed method can be used in practical SAR ATR systems trained using simulated images. Full article

(This article belongs to the Special Issue Radar Signal Detection, Recognition and Identification)

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20 pages, 1496 KiB

Open AccessArticle

Estimation and Classification of NLFM Signals Based on the Time–Chirp Representation

by Ewa Swiercz, Dariusz Janczak and Krzysztof Konopko

Sensors 2022, 22(21), 8104; https://doi.org/10.3390/s22218104 - 22 Oct 2022

Cited by 10 | Viewed by 2104

Abstract

A new approach to the estimation and classification of nonlinear frequency modulated (NLFM) signals is presented in the paper. These problems are crucial in electronic reconnaissance systems whose role is to indicate what signals are being received and recognized by the intercepting receiver. NLFM signals offer a variety of useful properties not available for signals with linear frequency modulation (LFM). In particular, NLFM signals can ensure the desired reduction of sidelobes of an autocorrelation (AC) function and desired power spectral density (PSD); therefore, such signals are more frequently used in modern radar and echolocation systems. Due to their nonlinear properties, the discussed signals are difficult to recognize and therefore require sophisticated methods of analysis, estimation and classification. NLFM signals with frequency content varying with time are mainly analyzed by time–frequency algorithms. However, the methods presented in the paper belong to time–chirp domain, which is relatively rarely cited in the literature. It is proposed to use polynomial approximations of nonlinear frequency and phase functions describing signals. This allows for applying the cubic phase function (CPF) as an estimator of phase polynomial coefficients. Originally, the CPF involved only third-order nonlinearities of the phase function. The extension of the CPF using nonuniform sampling is used to analyse the higher order polynomial phase. In this paper, a sixth order polynomial is considered. It is proposed to estimate the instantaneous frequency using a polynomial with coefficients calculated from the coefficients of the phase polynomial obtained by CPF. The determined coefficients also constitute the set of distinctive features for a classification task. The proposed CPF-based classification method was examined for three common NLFM signals and one LFM signal. Two types of neural network classifiers: learning vector quantization (LVQ) and multilayer perceptron (MLP) are considered for such defined classification problem. The performance of both the estimation and classification processes was analyzed using Monte Carlo simulation studies for different SNRs. The results of the simulation research revealed good estimation performance and error-free classification for the SNR range encountered in practical applications. Full article

(This article belongs to the Special Issue Radar Signal Detection, Recognition and Identification)

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13 pages, 4433 KiB

Open AccessCommunication

Radar Detection-Inspired Signal Retrieval from the Short-Time Fourier Transform

by Karol Abratkiewicz

Sensors 2022, 22(16), 5954; https://doi.org/10.3390/s22165954 - 9 Aug 2022

Cited by 2 | Viewed by 3431

Abstract

This paper presents a novel adaptive algorithm for multicomponent signal decomposition from the time–frequency (TF) plane using the short-time Fourier transform (STFT). The approach is inspired by a common technique used within radar detection called constant false alarm rate (CFAR). The areas with the strongest magnitude are detected and clustered, allowing for TF mask creation and filtering only those signal modes that contribute the most. As a result, one can extract a particular component void of noise and interference regardless of the signal character. The superiority understood as an improved reconstructed waveform quality of the proposed method is shown using both simulated and real-life radar signals. Full article

(This article belongs to the Special Issue Radar Signal Detection, Recognition and Identification)

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23 pages, 4468 KiB

Open AccessArticle

Multi-Sensory Data Fusion in Terms of UAV Detection in 3D Space

by Janusz Dudczyk, Roman Czyba and Krzysztof Skrzypczyk

Sensors 2022, 22(12), 4323; https://doi.org/10.3390/s22124323 - 7 Jun 2022

Cited by 20 | Viewed by 4845

Abstract

The paper focuses on the problem of detecting unmanned aerial vehicles that violate restricted airspace. The main purpose of the research is to develop an algorithm that enables the detection, identification and recognition in 3D space of a UAV violating restricted airspace. The proposed method consists of multi-sensory data fusion and is based on conditional complementary filtration and multi-stage clustering. On the basis of the review of the available UAV detection technologies, three sensory systems classified into the groups of passive and active methods are selected. The UAV detection algorithm is developed on the basis of data collected during field tests under real conditions, from three sensors: a radio system, an ADS-B transponder and a radar equipped with four antenna arrays. The efficiency of the proposed solution was tested on the basis of rapid prototyping in the MATLAB simulation environment with the use of data from the real sensory system obtained during controlled UAV flights. The obtained results of UAV detections confirmed the effectiveness of the proposed method and theoretical expectations. Full article

(This article belongs to the Special Issue Radar Signal Detection, Recognition and Identification)

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18 pages, 5815 KiB

Open AccessArticle

Pedestrian and Animal Recognition Using Doppler Radar Signature and Deep Learning

by Danny Buchman, Michail Drozdov, Tomas Krilavičius, Rytis Maskeliūnas and Robertas Damaševičius

Sensors 2022, 22(9), 3456; https://doi.org/10.3390/s22093456 - 1 May 2022

Cited by 10 | Viewed by 3207

Abstract

Pedestrian occurrences in images and videos must be accurately recognized in a number of applications that may improve the quality of human life. Radar can be used to identify pedestrians. When distinct portions of an object move in front of a radar, micro-Doppler signals are produced that may be utilized to identify the object. Using a deep-learning network and time–frequency analysis, we offer a method for classifying pedestrians and animals based on their micro-Doppler radar signature features. Based on these signatures, we employed a convolutional neural network (CNN) to recognize pedestrians and animals. The proposed approach was evaluated on the MAFAT Radar Challenge dataset. Encouraging results were obtained, with an AUC (Area Under Curve) value of 0.95 on the public test set and over 0.85 on the final (private) test set. The proposed DNN architecture, in contrast to more common shallow CNN architectures, is one of the first attempts to use such an approach in the domain of radar data. The use of the synthetic radar data, which greatly improved the final result, is the other novel aspect of our work. Full article

(This article belongs to the Special Issue Radar Signal Detection, Recognition and Identification)

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15 pages, 5885 KiB

Open AccessArticle

Multi-Criteria Decision Making to Detect Multiple Moving Targets in Radar Using Digital Codes

by Majid Alotaibi

Sensors 2022, 22(9), 3176; https://doi.org/10.3390/s22093176 - 21 Apr 2022

Cited by 1 | Viewed by 2089

Abstract

Technological advancement in battlefield and surveillance applications switch the radar investigators to put more effort into it, numerous theories and models have been proposed to improve the process of target detection in Doppler tolerant radar. However, still, more effort is needed towards the minimization of the noise below the radar threshold limit to accurately detect the target. In this paper, a digital coding technique is being discussed to mitigate the noise and to create clear windows for desired target detection. Moreover, multi-criteria of digital code combinations are developed using discrete mathematics and all designed codes have been tested to investigate various target detection properties such as the auto-correlation, cross-correlation properties, and ambiguity function using mat-lab to optimize and enhance the static and moving target in presence of the Doppler in a multi-target environment. Full article

(This article belongs to the Special Issue Radar Signal Detection, Recognition and Identification)

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26 pages, 3772 KiB

Open AccessArticle

An X-Band CMOS Digital Phased Array Radar from Hardware to Software

by Yue-Ming Wu, Hao-Chung Chou, Cheng-Yung Ke, Chien-Cheng Wang, Chien-Te Li, Li-Han Chang, Borching Su, Ta-Shun Chu and Yu-Jiu Wang

Sensors 2021, 21(21), 7382; https://doi.org/10.3390/s21217382 - 6 Nov 2021

Cited by 1 | Viewed by 4609

Abstract

Phased array technology features rapid and directional scanning and has become a promising approach for remote sensing and wireless communication. In addition, element-level digitization has increased the feasibility of complicated signal processing and simultaneous multi-beamforming processes. However, the high cost and bulky characteristics of beam-steering systems have prevented their extensive application. In this paper, an X-band element-level digital phased array radar utilizing fully integrated complementary metal-oxide-semiconductor (CMOS) transceivers is proposed for achieving a low-cost and compact-size digital beamforming system. An 8–10 GHz transceiver system-on-chip (SoC) fabricated in 65 nm CMOS technology offers baseband filtering, frequency translation, and global clock synchronization through the proposed periodic pulse injection technique. A 16-element subarray module with an SoC integration, antenna-in-package, and tile array configuration achieves digital beamforming, back-end computing, and dc–dc conversion with a size of 317 × 149 × 74.6 mm³. A radar demonstrator with scalable subarray modules simultaneously realizes range sensing and azimuth recognition for pulsed radar configurations. Captured by the suggested software-defined pulsed radar, a complete range–azimuth figure with a 1 km maximum observation range can be displayed within 150 ms under the current implementation. Full article

(This article belongs to the Special Issue Radar Signal Detection, Recognition and Identification)

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