Radar Signal Research Articles

Restoration of Automotive Radar Signals Under Mutual Interference by Digital In-Painting

Restoration of Automotive Radar Signals Under Mutual Interference by Digital In-Painting

An Improved Adaptive Radar Signal Sorting Algorithm Based on DBSCAN by a Novel CVI

An Improved Adaptive Radar Signal Sorting Algorithm Based on DBSCAN by a Novel CVI

Systems of quaternionic linear matrix equations: solution, computation, algorithm, and applications

<p>In applied and computational mathematics, quaternions are fundamental in representing three-dimensional rotations. However, specific types of quaternionic linear matrix equations remain few explored. This study introduces new quaternionic linear matrix equations and their necessary and sufficient conditions for solvability. We employ a methodology involving lemmas and ranks of coefficient matrices to develop a novel algorithm. This algorithm is validated through numerical examples, showing its applications in advanced fields. In control theory, these equations are used for analyzing control systems, particularly for spacecraft attitude control in aerospace engineering and for control of arms in robotics. In quantum computing, quaternionic equations model quantum gates and transformations, which are important for algorithms and error correction, contributing to the development of fault-tolerant quantum computers. In signal processing, these equations enhance multidimensional signal filtering and noise reduction, with applications in color image processing and radar signal analysis. We extend our study to include cases of $ \eta $-Hermitian and i-Hermitian solutions. Our work represents an advancement in applied mathematics, providing computational methods for solving quaternionic matrix equations and expanding their practical applications.</p>

A Low-Complexity Deep Learning Model for Modulation Classification and Detection of Radar Signals

A Low-Complexity Deep Learning Model for Modulation Classification and Detection of Radar Signals

Parameter Extraction Method of Overlapping Radar Signals Using Modulation Recognition-Guided Semantic Segmentation

Parameter Extraction Method of Overlapping Radar Signals Using Modulation Recognition-Guided Semantic Segmentation

Deep Optimized Broad Learning System for Applications in Tabular Data Recognition.

The broad learning system (BLS) is a versatile and effective tool for analyzing tabular data. However, the rapid expansion of big data has resulted in an overwhelming amount of tabular data, necessitating the development of specialized tools for effective management and analysis. This article introduces an optimized BLS (OBLS) specifically tailored for big data analysis. In addition, a deep-optimized BLS (DOBLS) network is developed further to enhance the performance and efficiency of the OBLS. The main contributions of this article are: 1) by retracing the network's error from the output space to the latent space, the OBLS adjusts parameters in the feature and enhancement node layers. This process aims to achieve more resilient representations, resulting in improved performance; 2) the DOBLS is a multilayered structure consisting of multiple OBLSs, wherein each OBLS connects to the input and output layers, enabling direct data propagation. This design helps reduce information loss between layers, ensuring an efficient flow of information throughout the network; and 3) the proposed methods demonstrate robustness across various applications, including multiview feature embedding, one-class classification (OCC), camera model identification, electroencephalogram (EEG) signal processing, and radar signal analysis. Experimental results validate the effectiveness of the proposed models. To ensure reproducibility, the source code is available at https://github.com/1027051515/OBLS_DOBLS.

Improving human activity classification based on micro-doppler signatures of FMCW radar with the effect of noise.

Nowadays, classifying human activities is applied in many essential fields, such as healthcare, security monitoring, and search and rescue missions. Radar sensor-based human activity classification is regarded as a superior approach in comparison to other techniques, such as visual perception-based methodologies and wearable gadgets. However, noise usually exists throughout the process of extracting raw radar signals, decreasing the quality and reliability of the extracted features. This paper presents a novel method for removing white Gaussian noise from raw radar signals using a denoising algorithm before classifying human activities using a deep convolutional neural network (DCNN). Specifically, the denoising algorithm is used as a preprocessing step to remove white Gaussian noise from the input raw radar signal. After that, a lightweight Cross-Residual Convolutional Neural Network (CRCNN) with adaptable cross-residual connections is suggested for classification. The analysis results show that the denoising algorithm with a range-bin interval of 3 and a cut-threshold value of 3 achieves the best denoising effect. When the denoising algorithm was applied to the dataset, CRCNN improved the right classification rate by up to 10% compared to the recognition results achieved with the original noise-added dataset. Additionally, a comparison of the CRCNN with the denoising algorithm solution with six cutting-edge DCNNs was conducted. The experimental results reveal that the proposed model greatly outperforms the others.

Radar Signal Modulation Recognition With Self-Supervised Contrastive Learning

Radar Signal Modulation Recognition With Self-Supervised Contrastive Learning

Syntactic modeling and neural based parsing for multifunction radar signal interpretation

Syntactic modeling and neural based parsing for multifunction radar signal interpretation

Proposal for Real-time Quality Control Method to Enhance Hydrological Application of Radar Rainfall

The primary aim of a rain radar is to observe ground-level rainfall accurately. Effective quality control of rain radar data is necessary to ensure accuracy. In the field of hydrology, various methods have been employed to improve the quality of radar rainfall by correcting or synthesizing it with gauge rainfall. However, it is more important to enhance the quality of radar polarimetric variables in order to improve the quality of radar rainfall. Rain radar systems generate various polarimetric variables through complex radar hardware systems and signal processing algorithms. Measurement errors may arise from equipment abnormalities, inappropriate operating settings, and obstructions in observation during signal processing algorithms and production process of polarimetric variables. Because rain radar is an observation device operating in real time, it entails more frequent and continuous quality control compared to flowmeters that intermittently measure streamflow. This study introduces a series of real-time quality control methods tailored for rain radar, including inspection of radar rainfall accuracy, quality analysis of polarimetric variables (ρ<sub>HV</sub>, Z<sub>DR</sub>, Φ<sub>DP</sub>), error estimation of polarimetric variables, and long-term variability of errors.

Studying sinkholes of the earth’s surface involving radar satellite interferometry in terms of Zhezkazgan field, Kazakhstan

Purpose is to assess the potential of radar satellite interferometry (SAR interferometry) to analyze and forecast earth’s surface displacements. Methods. The study involves the analysis of previous instrumental observations, such as high-precision levelling and seismic monitoring. The observations using global navigation satellite systems (GNSS) were carried out; satellite images were processed applying a method of sequential interferometry of persistent radar signal scatters (PS). Findings. The research results have proved similarity between the data obtained with the help of ground instrumental methods and the data received using satellite interferometry. Two types of the earth’s surface sinkholes were identified: sinkholes with smooth subsidence and hidden deformations that are not accompanied by the preliminary deformational or seismic signs. Smooth subsidence is controlled and predicted successfully with the help of the SAR interferometry methods. An algorithm has been represented to predict linear displacement trends at different time intervals involving a finite element method. Originality is in a complex approach of the research performance covering the following: comparative analysis of different monitoring methods, studying different sinkhole types, identifying limitations of the available methods, and proposing new approaches for more accurate and objective analysis of the earth’s surface deformation within the field. Practical implications. The research results are of practical value for rock mechanic specialists and mining operators. They can use the data to monitor and control earth’s surface caving as well as provide staff safety and preserve ground infrastructure where it is possible.

Intelligent Target Classification Algorithm for 77G Radar Based on Correction Data Set

Traffic participant classification is crucial in autonomous driving perception. Millimeter wave radio detection and ranging radar is a cost-effective and powerful method to perform the task in adverse traffic scenarios, especially in bad inclement weather (e.g. fog, snow and rain) and poor lighting conditions. This paper presents an intelligent target classification algorithm for 77G radar based on correction data set. First, in order to handle the problem that the original data set may easily be interfered by obstacles, the angle information is filtered by analyzing the spatial information of radar signals, which means the interference clutter of obstacles can be effectively removed. Second, the primary data set is corrected using the significant difference between the micro-Doppler of the human body and car. Finally, the characteristic information of the radar signal is extracted, including distance, speed, orientation, micro-Doppler and reflection intensity, and the obtained data sets containing three types of targets (vehicles, human bodies and obstacles) are generated. The generated dynamic and static data sets are collected by sufficient experiments to construct deep learning classification models. The results show that the classification accuracy is improved by the measure of data set correction.

Mathematical Model for a Radar Signal Reflected from Drone Propellers as Applied to the Method of Inverse Synthetic Aperture Radar in Bistatic Radar

Introduction. The distinction of targets located in the same spatial resolution cell of a radar system includes the determination of the number of targets and their recognition. Recognition and distinction are directly related to the analysis of radar profiles (spectral, range, azimuth, etc.). Radar images of rotating drone elements obtained with the method of inverse synthetic aperture radar (ISAR) present particular interest. Such profiles are highly informative in terms of defining the drone design characteristics. When developing algorithms for constructing radar profiles of drone propellers based on ISAR, it is necessary to have a clear understanding of the movements of various points on the propeller blade surfaces. This understanding can be achieved by constructing a mathematical model for a signal reflected from drone propellers.Aim. To develop a mathematical model for a signal reflected from drone propellers in application to the method of ISAR in bistatic radar.Materials and methods. In the model under consideration, the propeller blade is represented by a set of point reflectors located along two lines passing through the front and rear edges of the blade. When developing the reflected signal model, variation in the phase structure of the reflected signal arising due to the translational motion of the drone and the rotation of its propeller blades, as well as their offset in space.Results. A mathematical model for a signal reflected from drone propellers in application to the method of ISAR in bistatic radar was developed. Signals reflected from one propeller blade, from one propeller, and from a set of drone propellers were simulated. The temporal and spectral structures of the reflected signals for two variants of blade representation were analyzed.Conclusion. The developed mathematical model can be used when developing an algorithm for constructing images of drone propellers by the method of inverse synthetic aperture radar in a bistatic radar system.

Radar Emitter Signal Intra-Pulse Modulation Open Set Recognition Based on Deep Neural Network

Radar emitter signal intra-pulse modulation recognition is important for modern electronic reconnaissance systems to analyze target radar systems. In the actual environment, the intra-pulse modulations of the sampled radar emitter signals contain not only the known types in the library but also the unknown types. Therefore, the existing recognition methods, which are based on a closed set, cannot recognize the unknown samples. In order to solve this problem, in this paper, we proposed a method for radar emitter signal intra-pulse modulation open set recognition. The proposed method could classify the known modulations and identify the unknown modulation by using an original deep neural network-based recognition model trained on a closed set, estimating the signal-to-noise ratio, and calculating the reconstruction loss by an encoder–decoder model. For a given sample, the original deep neural network-based recognition model will label it as a certain known class temporarily. By estimating the SNR of the sample and calculating the reconstruction loss by inputting the sample to the corresponding encoder–decoder model related to the temporary predicted known class, whether the sample belongs to the predicted temporary known class or the unknown class will be confirmed. Experiments were conducted with five different openness conditions. The experimental results indicate that the proposed method has good performance on radar emitter signal intra-pulse modulation open set recognition.

Substantiation of source data on the parametric algorithms for the classification of weather hazards

The meteorological situation is one of the decisive factors determining the safety and frequency of civil aviation flights. Weather hazards (WH), associated with cumulonimbus clouds, such as a heavy shower, thunderstorm, hail, combined with high atmosphere turbulence, quite often lead to aviation events and even accidents. Currently, a domestic weather radar system of the near airfield zone (WR) “Monocle” has been developed and successfully operated. The criteria for the classification of meteorological phenomena (MP), used in the WR, have been developed individually for each phenomenon and have some heuristic character. These criteria are cumbersome and complicate the process of automating the WH classification. In this case, there is a natural desire to generalize the criteria and optimize them in accordance with the theory of distinguishing statistical hypotheses. This article discusses the application of the Bayesian approach to the WH classification. The statistical Bayesian decision theory assumes decision-making in terms of the probability theory when all significant probabilistic values, so-called sufficient statistics, are known. In order to obtain statistical descriptions of the probability distributions of reflectivity and the eddy dissipation rate (EDR), an analysis of radar signals, reflected from such MP as a rain shower, thunderstorm, hail was carried out. The article provides brief descriptions of the methods of conducting experiments to form statistical database and its analysis. Based on the above methods, the statistical parameter H(EDRmax) analysis for a rain shower, the amplitude distribution of reflectivity parameters and the EDR (Zmax, EDRmax) for thunderstorms and hail was carried out, which showed the low distinguishing ability of each individual parameter when solving the problem to classify MP within the assigned alphabet. The obvious solution is dictated by the theory of recognition. To increase the classification confidence, it is essential to share information parameters, for example, in the form of multidimensional distribution densities of the probabilities of random parameters. The article presents a parametric description of the MP “rain shower-thunderstorm-hail” classification features. An analysis to evaluate the probabilistic characteristics of the WH classification for the adopted empirical classification criteria in the WR shows that the adopted criteria are far from optimal in terms of the probabilities of the correct classification, especially in the rain shower case. It is obvious that a problem solution of the assigned classification confidence is associated with the optimization of the feature space and classification criteria. Based on the data obtained, it is necessary to build an algorithm to classify the WH “rain shower-thunderstorm-hail”.

Radar Signal Behavior in Maritime Environments: Falling Rain Effects

Precision modeling of radar signal behavior in maritime environments holds paramount importance in ensuring the robust functionality of maritime radar systems. This work delves into the intricate dynamics of radar signal propagation in maritime environments, with a particular focus on the effects of falling rain. A theoretical model encompassing raindrop scattering, gaseous absorption, and ocean surface backscattering was developed and validated. Key findings reveal that rain significantly alters radar backscattering, with a noticeable decrease in signal strength under higher rainrates. Additionally, gaseous absorption, particularly at elevated frequencies and humidity levels, emerged as a critical factor. The study also highlights the complex interplay between wind-induced ocean surface roughness and radar signal behavior. We think these insights are pivotal for enhancing maritime radar system accuracy, particularly in adverse weather conditions, and paving the way for future research in refining environmental impact models on radar signals.

A Deep Learning Method of Human Identification from Radar Signal for Daily Sleep Health Monitoring.

Radar signal has been shown as a promising source for human identification. In daily home sleep-monitoring scenarios, large-scale motion features may not always be practical, and the heart motion or respiration data may not be as ideal as they are in a controlled laboratory setting. Human identification from radar sequences is still a challenging task. Furthermore, there is a need to address the open-set recognition problem for radar sequences, which has not been sufficiently studied. In this paper, we propose a deep learning-based approach for human identification using radar sequences captured during sleep in a daily home-monitoring setup. To enhance robustness, we preprocess the sequences to mitigate environmental interference before employing a deep convolution neural network for human identification. We introduce a Principal Component Space feature representation to detect unknown sequences. Our method is rigorously evaluated using both a public data set and a set of experimentally acquired radar sequences. We report a labeling accuracy of 98.2% and 96.8% on average for the two data sets, respectively, which outperforms the state-of-the-art techniques. Our method excels at accurately distinguishing unknown sequences from labeled ones, with nearly 100% detection of unknown samples and minimal misclassification of labeled samples as unknown.

Non-contact sensing for anomaly detection in wind turbine blades: A focus-SVDD with complex-valued auto-encoder approach

The occurrence of manufacturing defects in wind turbine blade (WTB) production can result in significant increases in operation and maintenance costs of WTBs, reduce capacity factors of wind farms, and occasionally lead to severe and disastrous consequences. Therefore, inspection during the manufacturing process is crucial to ensure consistent fabrication of composite materials. Non-contact sensing techniques, such as Frequency Modulated Continuous Wave (FMCW) radar, are becoming increasingly popular as they offer a full view – cross sectional analysis – of these complex structures during assembly and curing. In this paper, we enhance the quality assurance of WTB manufacturing utilising FMCW radar as a non-destructive sensing modality. Additionally, a novel anomaly detection pipeline is developed that offers the following advantages: (1) We use the analytic representation of the Intermediate Frequency signal of the FMCW radar as a feature to disentangle material-specific and round-trip delay information from the received wave. (2) We propose a novel anomaly detection methodology called focus Support Vector Data Description (focus-SVDD). This methodology involves defining the limit boundaries of the dataset after removing healthy data features, thereby focusing on the attributes of anomalies. (3) The proposed method employs a complex-valued autoencoder to remove healthy features and we introduces a new activation function called Exponential Amplitude Decay (EAD). EAD takes advantage of the Rayleigh distribution, which characterises an instantaneous amplitude signal. The effectiveness of the proposed method is demonstrated through its application to collected data, where it shows superior performance compared to other state-of-the-art unsupervised anomaly detection methods. This method is expected to make a significant contribution not only to structural health monitoring but also to the field of deep complex-valued data processing and SVDD application. The code and dataset will be made publicly available. The code and dataset are available here 11https://github.com/FrusqueGaetan/Focus_SVDD_EAD.

Polarimetric Radar Signatures in Various Lightning Activities During Seoul (Korea) Flood on August 8, 2022

On August 8 and 9, 2022, a record-breaking rain rate of 142 mm h−1, with an accumulated rainfall of more than 500 mm, was observed in the Seoul metropolitan area, Republic of Korea. This study focuses on analyzing the concentration of lightning in southern Seoul, which occurred solely on August 8. It is worth noting that the daily rainfall of August 8 was approximately twice that of August 9 (381 mm on August 8 vs. 198 mm on August 9). The RKSG (located in Yongin, 40 km south of Seoul) Weather Surveillance Radar-1988 Doppler was used to explore the characteristics of cloud microphysics associated with lightning activity. Four major heavy rain periods on August 8 were grouped into three categories of lightning rate (e.g., intense, moderate, and none), and their polarimetric signatures were compared. Significant differences in the vertical distribution of graupel were found within the temperature range of 0 °C and − 20 °C, as indicated by radar reflectivity (ZH) > 40 dBZ and differential reflectivity (ZDR) < 0.5 dB. Although graupel was detected in all three categories at the relatively warm temperatures of 0 °C to − 10 °C, its presence extended into colder regions exclusively in the intense category. This observation preceded the appearance of lightning by approximately 6 min. At heights with temperature ≤ − 20 °C, a high concentration of vertically aligned ice crystals was observed in lightning-prone regions, leading to a decrease in differential phase (ΦDP). In summary, this study provides valuable insights into the microphysical characteristics of thunderstorms and their relationship to lightning activity in the Seoul metropolitan area.

Semhybridnet: a semantically enhanced hybrid CNN-transformer network for radar pulse image segmentation

Radar signal sorting is a vital component of electronic warfare reconnaissance, serving as the basis for identifying the source of radar signals. However, traditional radar signal sorting methods are increasingly inadequate and computationally complex in modern electromagnetic environments. To address this issue, this paper presents a novel machine-learning-based approach for radar signal sorting. Our method utilizes SemHybridNet, a Semantically Enhanced Hybrid CNN-Transformer Network, for the classification of semantic information in two-dimensional radar pulse images obtained by converting the original radar data. SemHybridNet incorporates two innovative modules: one for extracting period structure features, and the other for ensuring effective integration of local and global features. Notably, SemHybridNet adopts an end-to-end structure, eliminating the need for repetitive looping over the original sequence and reducing computational complexity. We evaluate the performance of our method through conducting comprehensive comparative experiments. The results demonstrate our method significantly outperforms the traditional methods, particularly in environments with high missing and noise pulse rates. Moreover, the ablation studies confirm the effectiveness of these two proposed modules in enhancing the performance of SemHybridNet. In conclusion, our method holds promise for enhancing electronic warfare reconnaissance capabilities and opens new avenues for future research in this field.