Characteristics Of Radar Signal Research Articles

Co-Frequency Interference Suppression of Integrated Detection and Jamming System Based on 2D Sparse Recovery

The integrated detection and jamming system employs integrated signals devoid of typical radar signal characteristics for detection and jamming. This allows for the sharing of resources such as waveform, frequency, time, and aperture, significantly enhancing the overall utilization rate of system resources. However, to achieve effective interference, the integrated waveform must overlap with the adversary radar signal within the frequency band. Consequently, the detection echoes are susceptible to the strong co-frequency direct wave generated by the adversary signals. This paper proposes a co-frequency direct wave interference suppression algorithm based on 2D generalized smoothed-l0 norm sparse recovery. The algorithm exploits a joint dictionary comprising our integrated signals and adversary signals, along with the sparsity of 2D range-Doppler maps. The direct solution of the sparse decomposition optimization problem, formulated for the entire echo matrix, enhances the target detection performance for integrated signals even in the presence of robust co-frequency direct wave interference. Furthermore, the proposed method achieves robustness to interference of varying intensities through the adaptive updating and adjustment of relevant parameters. The effectiveness of the proposed method is validated through simulation and experimental results.

Attention-Enhanced Dual-Branch Residual Network with Adaptive L-Softmax Loss for Specific Emitter Identification under Low-Signal-to-Noise Ratio Conditions

To address the issue associated with poor accuracy rates for specific emitter identification (SEI) under low signal-to-noise ratio (SNR) conditions, where the single-dimension radar signal characteristics are severely affected by noise, we propose an attention-enhanced dual-branch residual network structure based on the adaptive large-margin Softmax (ALS). Initially, we designed a dual-branch network structure to extract features from one-dimensional intermediate frequency data and two-dimensional time–frequency images, respectively. By assigning different attention weights according to their importance, these features are fused into an enhanced joint feature for further training. This approach enables the model to extract distinctive features across multiple dimensions and achieve good recognition performance even when the signal is affected by noise. In addition, we have introduced L-Softmax to replace the original Softmax and propose the ALS. This approach adaptively calculates the classification margin decision parameter based on the angle between samples and the classification boundary and adjusts the margin values of the sample classification boundaries; it reduces the intra-class distance for the same class while increasing the inter-class distance between different classes without the need for cumbersome experiments to determine the optimal value of decision parameters. Our experimental findings revealed that, in comparison to alternative methods, our proposed approach markedly enhances the model’s capability to extract features from signals and classify them in low-SNR environments, thereby effectively diminishing the influence of noise. Notably, it achieves the highest recognition rate across a range of low-SNR conditions, registering an average increase in recognition rate of 4.8%.

Radar antenna scanning type identification based on the fusion of multiple temporal features

Identifying the antenna scanning type (AST) of radar signal aims to deeply analyze the parameters of radar waveform and accurately judge the AST of the radar signal. Traditional methods rely heavily on domain priors and expert experience to set classification thresholds, which introduces human errors. Moreover, the generalization and anti-noise interference abilities are relatively weak, and it is difficult to deal with complex battlefield environments. The model based on the convolutional neural network (CNN) is limited by the receptive field of the convolution kernel, which makes the model unable to make full use of the global information of the signal. To fully exploit the global timing characteristics of radar signals, we propose a radar AST recognition method based on the fusion of multiple temporal features. Specifically, for the time series characteristics of the radar antenna scanning signal, a 1D CNN branch is first built to extract its short time series features. Given the limited receptive field of the convolutional network and the inability to fully consider the overall characteristics of the radar signal, we propose a transformer network branch to extract the global timing features fully. The recognition of radar AST based on multi-time series feature fusion can fully model the local and global sequence attributes, thereby improving the cognitive recognition ability of radar signals. Our method attains state-of-the-art results based on experimental findings.

RadarFormer: End-to-End Human Perception With Through-Wall Radar and Transformers.

For fine-grained human perception tasks such as pose estimation and activity recognition, radar-based sensors show advantages over optical cameras in low-visibility, privacy-aware, and wall-occlusive environments. Radar transmits radio frequency signals to irradiate the target of interest and store the target information in the echo signals. One common approach is to transform the echoes into radar images and extract the features with convolutional neural networks. This article introduces RadarFormer, the first method that introduces the self-attention (SA) mechanism to perform human perception tasks directly from radar echoes. It bypasses the imaging algorithm and realizes end-to-end signal processing. Specifically, we give constructive proof that processing radar echoes using the SA mechanism is at least as expressive as processing radar images using the convolutional layer. On this foundation, we design RadarFormer, which is a Transformer-like model to process radar signals. It benefits from the fast-/slow-time SA mechanism considering the physical characteristics of radar signals. RadarFormer extracts human representations from radar echoes and handles various downstream human perception tasks. The experimental results demonstrate that our method outperforms the state-of-the-art radar-based methods both in performance and computational cost and obtains accurate human perception results even in dark and occlusive environments.

A Novel Batch Streaming Pipeline for Radar Emitter Classification

In electronic warfare, radar emitter classification plays a crucial role in identifying threats in complex radar signal environments. Traditionally, this has been achieved using heuristic-based methods and handcrafted features. However, these methods struggle to adapt to the complexities of modern combat environments and varying radar signal characteristics. To address these challenges, this paper introduces a novel batch streaming pipeline for radar emitter classification. Our pipeline consists of two key components: radar deinterleaving and radar pattern recognition. We leveraged the DBSCAN algorithm and an RNN encoder, which are relatively light and simple models, considering the limited hardware resource environment of a military weapon system. Although we chose to utilize lightweight machine learning and deep learning models, we designed our pipeline to perform optimally through hyperparameter optimization of each component. We demonstrate the effectiveness of our proposed model and pipeline through experimental validation and analysis. Overall, this paper provides background knowledge on each model, introduces the proposed pipeline, and presents experimental results.

LPI Radar Detection Based on Deep Learning Approach with Periodic Autocorrelation Function.

In electronic warfare systems, detecting low-probability-of-intercept (LPI) radar signals poses a significant challenge due to the signal power being lower than the noise power. Techniques using statistical or deep learning models have been proposed for detecting low-power signals. However, as these methods overlook the inherent characteristics of radar signals, they possess limitations in radar signal detection performance. We introduce a deep learning-based detection model that capitalizes on the periodicity characteristic of radar signals. The periodic autocorrelation function (PACF) is an effective time-series data analysis method to capture the pulse repetition characteristic in the intercepted signal. Our detection model extracts radar signal features from PACF and then detects the signal using a neural network employing long short-term memory to effectively process time-series features. The simulation results show that our detection model outperforms existing deep learning-based models that use conventional autocorrelation function or spectrogram as an input. Furthermore, the robust feature extraction technique allows our proposed model to achieve high performance even with a shallow neural network architecture and provides a lighter model than existing models.

A Cascade Network for Pattern Recognition Based on Radar Signal Characteristics in Noisy Environments

Target recognition mainly focuses on three approaches: optical-image-based, echo-detection-based, and passive signal-analysis-based methods. Among them, the passive signal-based method is closely integrated with practical applications due to its strong environmental adaptability. Based on passive radar signal analysis, we design an “end-to-end” model that cascades a noise estimation network with a recognition network to identify working modes in noisy environments. The noise estimation network is implemented based on U-Net, which adopts a method of feature extraction and reconstruction to adaptively estimate the noise mapping level of the sample, which can help the recognition network to reduce noise interference. Focusing on the characteristics of radar signals, the recognition network is realized based on the multi-scale convolutional attention network (MSCANet). Firstly, deep group convolution is used to isolate the channel interaction in the shallow network. Then, through the multi-scale convolution module, the finer-grained features of the signal are extracted without increasing the complexity of the model. Finally, the self-attention mechanism is used to suppress the influence of low-correlation and negative-correlation channels and spaces. This method overcomes the problem of the conventional method being seriously disturbed by noise. We validated the proposed method in 81 kinds of noise environment, achieving an average accuracy of 94.65%. Additionally, we discussed the performance of six machine learning algorithms and four deep learning algorithms. Compared to these methods, the proposed MSCANet achieved an accuracy improvement of approximately 17%. Our method demonstrates better generalization and robustness.

Recognition of Approximate Motions of Human Based on Micro-Doppler Features

With the aging society approaching, the daily safety of the elderly has attracted more and more people’s attention, especially the elderly who live alone without anyone to take care of them. How to judge whether there is danger from the daily behavior of those who live alone and providing timely assistance is a research topic of health protection in the field of smart home care. Modern radar sensing technology and intelligent perception technology adopted in the domain of health care have started to gain significant interest, which can replace wearable sensors or cameras without feeling uncomfortable or having privacy issues. In this paper, some preliminary results were presented to develop an indoor monitoring system for human activity recognition using micro-Doppler features of radar signals. The experimental campaign involved five different with some similar activities of 10 subjects to be collected using a Doppler radar. A total of several texture features and numerical features are extracted from the spectrogram and time-frequency domain of measurements and applied three machine learning (ML) algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree (D-Tree) for the precise classification of all five different motions. The results illustrated that the SVM classifier using the Radial Basis Function (RBF) kernel reaches the best results for all three feature sets, and the classification of mixed features can reach 93.2%, as well as 88.4% and 92.4% for numerical and texture features. The performance of SVM is 1.6% better than KNN and 3.2% better than D-Tree.

Soil Moisture Sensing with UAV-Mounted IR-UWB Radar and Deep Learning

Wide-area soil moisture sensing is a key element for smart irrigation systems. However, existing soil moisture sensing methods usually fail to achieve both satisfactory mobility and high moisture estimation accuracy. In this paper, we present the design and implementation of a novel soil moisture sensing system, named as SoilId, that combines a UAV and a COTS IR-UWB radar for wide-area soil moisture sensing without the need of burying any battery-powered in-ground device. Specifically, we design a series of novel methods to help SoilId extract soil moisture related features from the received radar signals, and automatically detect and discard the data contaminated by the UAV's uncontrollable motion and the multipath interference. Furthermore, we leverage the powerful representation ability of deep neural networks and carefully design a neural network model to accurately map the extracted radar signal features to soil moisture estimations. We have extensively evaluated SoilId against a variety of real-world factors, including the UAV's uncontrollable motion, the multipath interference, soil surface coverages, and many others. Specifically, the experimental results carried out by our UAV-based system validate that SoilId can push the accuracy limits of RF-based soil moisture sensing techniques to a 50% quantile MAE of 0.23%.

Symbol State Transition Entropy: A Novel Feature of Interpulse Radio Frequency Modulation Signal

In radar electronic warfare (EW) reconnaissance, radio frequency (RF) is an important parameter for describing the received pulse, and it is usually used in sorting and identifying radar signals. In order to achieve different detection purposes, different modulations are used in intra-pulse and interpulse radar signals. It is an important frequency modulation mode in radar signals for interpulse frequency modulation, which describes the characteristics of dynamic changes of radar pulse signals. However, it is difficult to describe these characteristics effectively by only using Pulse Description Word(PDW). In this letter, a novel feature, based on symbolization and entropy, is proposed to characterize interpulse frequency modulation characteristics of radar signals. Simulation results show that the proposed feature not only have an excellent performance of describing interpulse RF modulation radar signals, but is also insensitive to the environment with a certain rate of pulses missing and spurious pulses. Besides, in terms of time complexity, it also has a satisfactory performance.

Research and Application of Compact Air-Coupled Ground Penetrating Radar in Thin-Layer Asphalt Thickness Testing and Vibration Resistant Calibration Method

For high-precision continuous thickness measurement of thin-layer asphalt loose paving, the paper proposes a compact ground penetrating radar (GPR) solution with smaller volume than the conventional air-coupled antenna. It takes into account the minimum identifiable thickness of the GPR tailing characteristics of radar signals, the common thickness range of thin-layer asphalt, and the vibration of construction machinery. In general, the overlap between bottom reflection and surface reflection of thin-layer asphalt can be solved by subtracting the reflection of the asphalt surface from the calibration waveform file of a metal plate. However, because the operating environment of asphalt paving is not suitable to realize the frequent static calibration of metal plates, the paper designs a bow-tie antenna loaded with high loss absorbent material substrate, which significantly improves the signal tailing phenomenon and greatly reduces the size of the air-coupled antenna. At the same time, in view of dynamic change of paver vibration and the requirements of the construction site, less calibration is more advantageous. Based on the full-amplitude reflection method, a fitting formula for the dielectric constant of asphalt suitable for mechanical vibration is proposed in this paper, to further improve the engineering applicability of the detection system. Finally, static calibration asphalt thickness, dynamic measured dielectric constant asphalt thickness, and manual sampling thickness under three construction conditions are compared, and the capability of the compact air-coupled GPR detection system proposed in this paper is comprehensively verified.

Lamb Wave Frequency Diverse Array.

The imaging method based on the conventional phased array (PA) can only realize angle focusing. To break through the above limitations, frequency diverse array (FDA) is first introduced from radar to Lamb wave, which opens up a possibility to realize angle-range focusing in damage localization. The energy pattern of FDA without considering the features of radar signals is re-derived, and a corresponding FDA focusing imaging method for Lamb wave is developed. In the presented method, by appending various frequency offsets to the redesigned anti-dispersive excitations, the desired FDA focusing and the dispersion elimination for Lamb wave are achieved simultaneously. Furthermore, based on full matrix capture being the data acquisition approach, a novel total focusing is realized with comprehensive consideration of positive and negative frequency offsets. Benefiting from FDA focusing, compared with the PA methods, the proposed method exhibits superior adjacent defects' identification ability and preferable interference suppression capability with excellent location accuracy and strong parameter robustness. Its superiority is validated by the experiment on aluminum plate with multiple adjoining surface and inner defects.

Contrastive time–frequency learning for radar signal sorting

With the development of radar technology, the electromagnetic environment becomes more and more complex. The acquisition of labeled data is costly, and the existing radar signal sorting and recognition methods have poor generalization capability. To address this problem, a radar signal recognition technique based on time–frequency analysis and contrastive learning is proposed in this paper. The classification process is improved according to the characteristics of radar signals without using labels. The proposed method, contrastive time–frequency learning, can use the unsupervised data to sort and identify radar signals. By augmenting the data with various channel conditions, the generalization ability of the classification model is also improved, especially for the situation with low SNR. The experiments prove the good performance of the proposed method, and classification accuracy for radar signals can be up to 98.17%.

MATLAB simulation of performance evaluation model of time-frequency analysis method based on SVM

Time-frequency analysis is a prerequisite for intelligent recognition of radar signal types based on deep learning network. Deep learning uses Convolutional Neural Network (CNN) to automatically extract the time-frequency images (TFIs) features of radar signals to achieve intelligent recognition of radar signal modulation methods. However, the quality of TFI generated by different time-frequency conversions is usually different to a certain extent. At present, the selection of time-frequency analysis methods in many pieces of research is mainly done through the feature differences of TFIs. There are certain subjective factors, which cannot provide a reference for subsequent research. This paper proposes a time-frequency analysis method performance evaluation model based on Support Vector Machine (SVM). The simulation results show that the evaluation model can make an objective and optimal choice based on the data, and can provide a valuable reference for subsequent research.

Marine Distributed Radar Signal Identification and Classification Based on Deep Learning

Distributed radar is applied extensively in marine environment monitoring. In the early days, the radar signals are identified inefficiently by operators. It is promising to replace manual radar signal identification with machine learning technique. However, the existing deep learning neural networks for radar signal identification consume a long time, owing to autonomous learning. Besides, the training of such networks requires lots of reliable time-frequency features of radar signals. This paper mainly analyzes the identification and classification of marine distributed radar signals with an improved deep neural network. Firstly, the time frequency features were extracted from signals based on short-time Fourier transform (STFT) theory. Then, a target detection algorithm was proposed, which weighs and fuses the heterogenous marine distributed radar signals, and four methods were provided for weight calculation. After that, the frequency-domain priori model feature assistive training was introduced to train the traditional deep convolutional neural network (DCNN), producing a CNN with feature splicing operation. The features of time- and frequency-domain signals were combined, laying the basis for radar signal classification. Our model was proved effective through experiments.

Measurement of sea wave height by radar sounding with polarizationmodulated signals

Methods for determining the height of sea waves, which are relevant for maritime navigation, have been investigated. The measurement of the height of sea waves by the method of radar sounding by polarization-modulated signals is considered. The energy and spectral characteristics of the radar signal reflected by the sea surface at low angles of incidence are described. An algorithm for calculating the average wave height based on the results of measuring the polarization anisotropy of the specific effective scattering surface of the sea area is considered. The relation of the average frequency of the amplitude fluctuations of the reflected radar signals with the sea waves is given. The spectral characteristics of radar signals reflected by the sea surface when irradiated with polarization-modulated signals are analyzed. The procedure for forming an effective statistical estimate of the average wave height based on the results of spectral measurements is considered. The errors of the generated estimates of the wave height associated with the measurement errors of the polarization anisotropy of the effective scattering surface and the average frequency of the envelope of the amplitude fluctuations caused by the Doppler effect are analyzed. The results of experimental measurements of the spectra of the amplitude fluctuations of the envelope of the reflected signals at different states of the sea surface are presented. The analytical dependence of the polarization anisotropy of the effective sea scattering surface on the irradiation angle relative to the wave propagation direction is given. A comparison of estimates of the height of sea waves obtained from radar sounding and meteorological observation data is carried out.

Observation of quadrocopters by radar with long-term coherent signal accumulation

The article discusses the experimentally obtained characteristics of radar signals reflected from small-sized aerial targets such as a quadrocopter, with their long-term coherent accumulation. A brief description of the structural diagram of the experimental radar and its characteristics is given. The radar operates in the ten-centimeter wavelength range and emits a coherent-pulse signal. The duration of the emitted chirp pulse is 1 μs with a compression ratio of 15. Algorithms for primary processing of signals in a computer are given, including compression of chirp signals and spectral analysis of the received implementation, which is equivalent to its coherent accumulation. The parameters of the generated radar image are determined. The characteristics of the targets used - the small-sized quadcopters Mavic and Phoenix – are given. As a result of the experiments, it was shown that the tested small-sized air targets in the ten-centimeter wavelength range of the probing signal have their own coherence time sufficient for the coherent accumulation of the signal reflected from them for a time of at least 0.2 seconds. The Mavic does not produce reflections from its rotating rotors. The main rotor of the Phoenix quadcopter creates spectral components in the image, concentrated along the speed axis in the form of maxima symmetrically located relative to the central mark of the target. The presence of this feature of the signal allows you to identify the type of target, highlight the target against the background of birds, and detect a stationary, hovering target. It is shown that the features of signals reflected from the ground, with long-term coherent accumulation, allow providing the minimum speed of the detected target, measured in fractions of a meter per second.

RETRACTED: Application of radar signal processing and image display algorithm based on computer hardware system in intelligence processing

Hardware system based radar signal detection, specific progress and current limitations. As indicated by the radar signal's characteristics, the radar signal's plan and arrangement are introduced with the irregularities and favorable conditions of the mirror mark. The radar signal's multidisciplinary preparatory invention is equipped with a versatile radar signal cycle, beat signal administration, computerized sifting signal mode, and Doppler strategy indicating nuances. An image display algorithm based on the radar signal's transmission cycle is extracted, which includes the transmission stages of the radar signal, the factors affecting the radar signal transmission and the radar data screening. Planning technology for radar signal and related signal characteristics will be evaluated to improve implementation. Radar signal calibration strategy and related impact factors are additionally isolated and described. Radar signal handling is described in detail, including the Innovation Multidisciplinary Innovation compound. Versatile radar signal cycle beat pressure executives and computerized separating Doppler strategy are very effective specialized techniques with interesting features. Finally, the difficulties of future testing methods and the progress of radar signals are proposed.

Identification and parameter estimation algorithm of radar signal subtle features

With the rapid development of electronic technology and its widespread application in modern warfare, the modulation methods of individual radar signal sources are more flexible, the parameters are diverse, and the modern battlefield electromagnetic signal environment is complex, so that radar signal recognition based on traditional conventional parameters Technology cannot meet real needs. Among the many radar signal modulation recognition algorithms that have appeared in recent years, neural network theory and intra-pulse analysis algorithms are widely used, but they each have their own shortcomings, and a single method cannot meet the needs of increasingly complex modulation signal recognition and parameter estimation needs. . Based on the above background, the research content of this paper is to identify the subtle characteristics of radar signals and their parameter estimation algorithms. In this paper, two types of typical modulation signals in intentional modulation of radar signals, namely phase-coded (PSK) signals and frequency-modulated (FM) signals, are proposed. A SVEFD algorithm based on classification from coarse to fine. According to the characteristics of the 3 dB bandwidth of the frequency spectrum of the PSK signal and the FM signal, the coarse classification between the classes is performed first. Finally, through experimental simulations, the results show that the correct recognition probability of the SVEFD algorithm at a lower signal-to-noise ratio is much higher than the m algorithm and also higher than the SVD algorithm. When the signal-to-noise ratio is greater than 1 dB, the average recognition rate reaches 94%, which proves that The effectiveness of the proposed SVEFD algorithm. When the signal-to-noise ratio is .1 dB, in addition to maintaining the correct recognition rate of more than 90% for the LFM and COSTAS signals, the other six types of signals have low correct recognition rates, so the SVEFD algorithm has advantages in comparison.

A New Method for Analyzing the Attenuation Characteristics of Satellite Radar Altimeter Signals Due to Rainfall Based on a Multilayer Medium Model

AbstractRainfall has an attenuation of backscatter coefficient of the sea surface and a change in the sea surface roughness, and these effects give rise to some errors in retrieving the sea surface wind speed, the significant wave height, and wave slope. It is meaningful to make considerable efforts to study the attenuation of radio waves caused by rainfall. In this article, a new model of analyzing the attenuation characteristics of satellite radar altimeter pulse signals because of rainfall is presented. First, the model of the equivalent permittivity of rainfall is calculated and presented. The imaginary part of the equivalent permittivity is studied when radio frequency and rainfall rate vary, respectively. Second, a new theoretical model and method of analyzing the effect of rainfall on satellite radar altimeter pulses is presented based on a multilayer‐medium model with the equivalent permittivity of rainfall. On the other hand, some existing models of computing rainfall attenuation characteristics of radar signals are studied and improved to some extent. Finally, some attenuation characteristics of radar signals due to rainfall are calculated by those models. The calculated results show that the attenuation characteristics of radar altimeter pulse signals are all in good agreement with those by other models. The results computed by the multilayer‐medium model can provide a good theoretical explanation and understanding for these attenuation characteristics of radar pulse signals and can also give some data support and corrections for the measured oceanic parameters by the satellite radar altimeter.