High Resolution Range Profile Research Articles

Polarimetric HRRP recognition based on feature‐guided Transformer model

Polarimetric high-resolution range profile (HRRP) holds great potential for radar automatic target recognition (RATR) owing to its capability of providing both polarimetric and spatial scattering information. In recent years, deep learning (DL) has obtained state-of-the-art results in many classification tasks and has drawn great attention in the RATR field. However, as one of the most challenging tasks in RATR, small training sample case will restrict the application of DL because its superior performance generally depends on a large number of training samples. A feature-guided deep model based on Transformer framework is proposed for polarimetric HRRP recognition with limited training samples. In the proposed model, artificial features are introduced to the attention module to guide the model focus on the range cells of HRRP with more target scattering information so as to reduce the dependence of the model on the number of training samples. Several different approaches are also studied to measure the similarity between artificial features and HRRP data to further improve the learning capacity of the model. Experimental results demonstrate that the proposed feature-guided Transformer model modifying by Cosine similarity measure is able to achieve a better performance for polarimetric HRRP recognition with limited training samples.

Three-Dimensional Slope Imaging Method for Ground-Based Real-Aperture Radar.

Traditional two-dimensional radar images can only reflect the target azimuth and slant range and thus suffer problems of geometric deformation and overlapping. The unique three-dimensional (3D) imaging capability of ground-based real-aperture radar can more accurately and directly achieve correlation between the radar image and the slope monitoring scenarios, thus providing reliable information for the early warning and forecasting of landslides and collapse disasters. The latest method of selecting a slope target from a high-resolution range profile includes two indexes: maximum amplitude and coherence, which will affect the accuracy of displacement measurement when there is an interference target. We present a three-dimensional slope imaging method based on smoothness constraints. On the basis of the latest method, the objective fact of the practically smooth and continuous distribution of slope surfaces is considered. This method can be used for image interpretation on strongly scattered targets within the slope. The independently developed ground-based real-aperture slope radar system was deployed in the Heidaigou Open-Pit Coal Mine in Inner Mongolia to carry out 3D slope imaging experiments. The effectiveness of this method in slope monitoring and imaging was confirmed by comparing the surface roughness and the spatial positions of the targets with the high-density point cloud data in the projective plane obtained during the same time period. We used RMSE function and roughness as two measures. It shows that the method presented in this paper is more suitable for actual three-dimensional slope imaging.

Multi-scale feature vector reconstruction for aircraft classification using high range resolution radar signatures

High-Resolution Range Profile (HRRP) is effective in various Radar Automatic Target Recognition problems. Multi-scale techniques have been verified in aircraft target HRRP feature enrichment to achieve aircraft recognition performance optimization. The involvement of multiple training-classification procedures in existing multi-scale methods results in tremendous resource consumption which challenges their real-time classification performance and application significance. This paper introduces a novel method that enables multi-scale HRRP features to be manipulated under a single scale for efficiency enhancement. Numerical analysis indicates that multi-scale intensity variations for particular HRRP scatterers could be modeled as Gaussian noises. Linear Discriminant Analysis and Singular Value Decomposition techniques are applied on reconstructed feature vectors for better separability and noise tolerance capability. Experimental results from dynamic aircraft recognition experiments verify the expected efficiency enhancement of the proposed method while maintaining comparable classification accuracy and better noise tolerance performance.

4D Automotive Radar Sensing for Autonomous Vehicles: A Sparsity-Oriented Approach

We propose a high-resolution imaging radar system to enable high-fidelity four-dimensional (4D) sensing for autonomous driving, i.e., range, Doppler, azimuth, and elevation, through a joint sparsity design in frequency spectrum and array configurations. To accommodate a high number of automotive radars operating at the same frequency band while avoiding mutual interference, random sparse step-frequency waveform (RSSFW) is proposed to synthesize a large effective bandwidth to achieve high range resolution profiles. To mitigate high range sidelobes in RSSFW radars, optimal weights are designed to minimize the peak sidelobe level such that targets with a relatively small radar cross section are detectable without introducing high probability of false alarm. We extend the RSSFW concept to multi-input multi-output (MIMO) radar by applying phase codes along slow time to synthesize a two-dimensional (2D) sparse array with hundreds of virtual array elements to enable high-resolution direction finding in both azimuth and elevation. The 2D sparse array acts as a sub-Nyquist sampler of the corresponding uniform rectangular array (URA) with half-wavelength interelement spacing, and the corresponding URA response is recovered by completing a low-rank block Hankel matrix. Consequently, the high sidelobes in the azimuth and elevation spectra are greatly suppressed so that weak targets can be reliably detected. The proposed imaging radar provides point clouds with a resolution comparable to LiDAR but with a much lower cost. Numerical simulations are conducted to demonstrate the performance of the proposed 4D imaging radar system with joint sparsity in frequency spectrum and antenna arrays.

Study on feature processing schemes for deep‐learning‐based human activity classification using frequency‐modulated continuous‐wave radar

Radar is an attractive sensor for classifying human activity because of its invariance to the environment and its ability to operate under low lighting conditions and through obstacles. Classification for human activity finds applications in human–computer interfaces, user-intent understanding and contextual-aware smart homes. Moreover, frequency-modulated continuous-wave technology enables radar systems to retrieve both micro-Doppler and range profiles from humans, which can then be used to recognize target movements. Several feature processing schemes are studied on signatures obtained from a compact, short-range, 60-GHz, frequency-modulated continuous-wave radar for various human activities. The contribution of distinct features to the performance of classification is analysed and novel convolutional- and recurrent-neural-network architectures are consequently presented that efficiently combine range and Doppler information for classifying human activity. In particular, applying multibranch deep learning techniques to high-resolution range profiles and Doppler spectrograms achieves performance similar to that using range-Doppler map videos although it requires less computational and memory resources.

Time-Frequency Analysis and Target Recognition of HRRP Based on CN-LSGAN, STFT, and CNN

Aiming at the problem of radar target recognition of High-Resolution Range Profile (HRRP) under low signal-to-noise ratio conditions, a recognition method based on the Constrained Naive Least-Squares Generative Adversarial Network (CN-LSGAN), Short-time Fourier Transform (STFT), and Convolutional Neural Network (CNN) is proposed. Combining the Least-Squares Generative Adversarial Network (LSGAN) with the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), the CN-LSGAN is presented and applied to the HRRP denoise. The frequency domain and phase features of HRRP are gained by STFT in order to facilitate feature learning and also match the input data format of the CNN. These experimental results show that the CN-LSGAN has better data augmentation performance and can effectively avoid the model collapse compared to the generative adversarial network (GAN) and LSGAN. Also, the method has better recognition performance than the one-dimensional CNN method and the Long Short-Term Memory (LSTM) network method.

Application of the fruit fly optimization algorithm to an optimized neural network model in radar target recognition

With the development of computer technology, there are more and more algorithms and models for data processing and analysis, which brings a new direction to radar target recognition. This study mainly analyzed the recognition of high resolution range profile (HRRP) in radar target recognition and applied the generalized regression neural network (GRNN) model for HRRP recognition. In order to improve the performance of HRRP, the fruit fly optimization algorithm (FOA) algorithm was improved to optimize the parameters of the GRNN model. Simulation experiments were carried out on three types of aircraft. The improved FOA-GRNN (IFOA-GRNN) model was compared with the radial basis function (RBF) and GRNN models. The results showed that the IFOA-GRNN model had a better convergence accuracy, the highest average recognition rate (96.4 %), the shortest average calculation time (275 s), and a good recognition rate under noise interference. The experimental results show that the IFOA-GRNN model has a good performance in radar target recognition and can be further promoted and applied in practice.

W-Band Multi-Aspect High Resolution Range Profile Radar Target Classification Using Support Vector Machines.

Millimeter-wave (W-band) radar measurements were taken for two maritime targets instrumented with attitude and heading reference systems (AHRSs) in a littoral environment with the aim of developing a multiaspect classifier. The focus was on resource-limited implementations such as short-range, tactical, unmanned aircraft systems (UASs) and dealing with limited and imbalanced datasets. Radar imaging and preprocessing consisted of recording high-resolution range profiles (HRRPs) and performing range alignment using peak detection and fast Fourier transforms (FFTs). HRRPs were used because of their simplicity, reliability, and speed. The features used were fixed-length, frequency domain range profiles. Two linear support vector machine (SVM)-based classifiers were developed which both yielded excellent results in their general forms and were simple to implement. The first approach utilized the positive predictive value (PPV) and negative predictive value (NPV) statistics of the SVM directly to generate target probabilities and consequently determine the optimal aspect transitions for classification. The second approach used the Kolmogorov–Smirnov test for dimensionality reduction, followed by concatenating feature vectors across several aspects. The latter approach is particularly well-suited to resource-constrained scenarios, potentially allowing for retraining and updating in the field.

Radar High-Resolution Range Profile Ship Recognition Using Two-Channel Convolutional Neural Networks Concatenated with Bidirectional Long Short-Term Memory

Radar automatic target recognition is a critical research topic in radar signal processing. Radar high-resolution range profiles (HRRPs) describe the radar characteristics of a target, that is, the characteristics of the target that is reflected by the microwave emitted by the radar are implicit in it. In conventional radar HRRP target recognition methods, prior knowledge of the radar is necessary for target recognition. The application of deep-learning methods in HRRPs began in recent years, and most of them are convolutional neural network (CNN) and its variants, and recurrent neural network (RNN) and the combination of RNN and CNN are relatively rarely used. The continuous pulses emitted by the radar hit the ship target, and the received HRRPs of the reflected wave seem to provide the geometric characteristics of the ship target structure. When the radar pulses are transmitted to the ship, different positions on the ship have different structures, so each range cell of the echo reflected in the HRRP will be different, and adjacent structures should also have continuous relational characteristics. This inspired the authors to propose a model to concatenate the features extracted by the two-channel CNN with bidirectional long short-term memory (BiLSTM). Various filters are used in two-channel CNN to extract deep features and fed into the following BiLSTM. The BiLSTM model can effectively capture long-distance dependence, because BiLSTM can be trained to retain critical information and achieve two-way timing dependence. Therefore, the two-way spatial relationship between adjacent range cells can be used to obtain excellent recognition performance. The experimental results revealed that the proposed method is robust and effective for ship recognition.

Estimation of Complex High-Resolution Range Profiles of Ships by Sparse Recovery Iterative Minimization Method

It is always an important problem to recover sparse signals from observations corrupted by Gaussian noise and has been extensively investigated. In high-resolution maritime surveillance radars working at scan mode, ship classification, and recognition need to recover high-resolution range profiles (HRRPs) of ships from radar returns of several pulses with severe range sidelobe effect. Multipulse synthetic data by the aid of ship Doppler information are complex sparse signals corrupted by non-Gaussian correlated interference. In this article, a sparse recovery via iterative minimization (SRIM) method is proposed to estimate complex HRRPs of ships from multipulse synthetic data. The SRIM method adapts the non-Gaussianity nature of the interference in the multipulse synthetic data and ship complex HRRPs are modeled by the random sequences of the biparametric generalized Gaussian distributions (GGDs) (0 < <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> ≤ 1). In the SRIM method, the parameters of the GGD model are iteratively searched by the minimal criterion of the Kolmogorov–Smirnov distance of the residue and the interference model. The SRIM method is compared with the recent linear-programming-based method and the classic sparse learning via iterative minimization (SLIM) method by using simulated and measured radar data and the results show that the SRIM method obtains the better performance.

Radar Target Classification Based on High Resolution Range Profile Segmentation and Ensemble Classification

Radar target range profile possesses advantages in many target recognition problems. Feature extraction techniques could optimize target recognition performance, but conventional methods neglect specific characters of electromagnetic scattering mechanisms in range profiles. This letter introduces a range profile segmentation algorithm, which decomposes target range profile into segments corresponding to particular electromagnetic scattering mechanisms. Segment type identification and weighing algorithms are also developed for ensemble classification strategy. Results indicate that the proposed method could achieve comparable classification accuracy as advanced neural networks but requires far fewer computational resources to guarantee its application flexibility.

Synthetic wideband range profiling method for moving targets based on phase‐derived velocity measurement

Synthetic wideband signals can obtain a wide spectrum and thus achieve a high range resolution by synthesizing separately emitted signals. A synthetic wideband range profiling method for moving targets based on phase-derived velocity measurement is proposed. Highly accurate velocity measurement results are obtained by analysing the echo phase information of a moving target, and the accuracy of the velocity estimation results can meet the requirement of the velocity compensation in the synthetic wideband range profiling process. After analysis of the spectrum envelope and the phase of a moving target, a method for synthesizing wideband signals in the frequency domain, thus obtaining a high-resolution range profile, is developed. The constraint conditions for the motion compensation and synthesis process are analysed and discussed by simulation, and the effectiveness of the proposed method is verified under various circumstances.

Radar HRRP Target Recognition Based on Dynamic Learning with Limited Training Data

For high-resolution range profile (HRRP)-based radar automatic target recognition (RATR), adequate training data are required to characterize a target signature effectively and get good recognition performance. However, collecting enough training data involving HRRP samples from each target orientation is hard. To tackle the HRRP-based RATR task with limited training data, a novel dynamic learning strategy is proposed based on the single-hidden layer feedforward network (SLFN) with an assistant classifier. In the offline training phase, the training data are used for pretraining the SLFN using a reduced kernel extreme learning machine (RKELM). In the online classification phase, the collected test data are first labeled by fusing the recognition results of the current SLFN and assistant classifier. Then the test samples with reliable pseudolabels are used as additional training data to update the parameters of SLFN with the online sequential RKELM (OS-RKELM). Moreover, to improve the accuracy of label estimation for test data, a novel semi-supervised learning method named constraint propagation-based label propagation (CPLP) was developed as an assistant classifier. The proposed method dynamically accumulates knowledge from training and test data through online learning, thereby reinforcing performance of the RATR system with limited training data. Experiments conducted on the simulated HRRP data from 10 civilian vehicles and real HRRP data from three military vehicles demonstrated the effectiveness of the proposed method when the training data are limited.

Recognition-Aware HRRP Generation With Generative Adversarial Network

Existing works on radar high-resolution range profile (HRRP) recognition commonly focus on utilizing data and various deep learning models in achieving high classification accuracy. However, in practical applications, it is often difficult to obtain HRRP signals, especially for noncooperative targets. Such lack of data dramatically decreases the recognition performance, so this letter applies data augmentation to address small-sample problems. A recognition-aware HRRP generation framework based on a generative adversarial network is proposed for data augmentation, which generates discriminative samples by decomposing and reorganizing signal’s characteristics. The proposed model increases the generated signals’ discriminative power, thus meeting the application requirements. Experiments show that the generated HRRP signals can not only accurately expand the data set but also improve the recognition system’s performance. Besides, the developed model outperforms traditional data augmentation methods and other generative methods. To the best of our knowledge, this is the first work on HRRP signal generation in radar automatic target recognition systems.

High-Resolution ISAR Imaging Under Low SNR With Sparse Stepped-Frequency Chirp Signals

To obtain high-resolution imaging while reducing the radar operating bandwidth under a low signal-to-noise ratio (SNR), this article proposes a genetic method for accurate residual radial motion estimation and well-focused imaging of the sparse stepped-frequency chirp signal (SSFCS). First, the signal model is constructed by incorporating the residual radial motion parameters into the dictionary. Then, high-quality high-resolution range profiles (HRRPs) are synthesized by Beta process regression (BPR), which has enhanced flexibility in data description and superior performance in parameter estimation. In addition, the genetic method updates the population, i.e., the candidates for the residual radial motion parameters, iteratively according to the image entropy to meet the required precision for well-focused imaging. Experimental results of Monte Carlo simulations and imaging results of simulated and measured data have demonstrated that the proposed method achieves more accurate estimation in residual radial motion parameters and better-focused imaging than the available methods.

Pulse radar randomly interrupted transmitting and receiving optimization based on genetic algorithm in radio frequency simulation

The interrupted transmitting and receiving (ITR) can be used in anechoic chamber to solve the coupling between the transmitted and reflected signals. When the ITR periods are random, the fake peaks in high-resolution range profile (HRRP) of ITR echo can be reduced. Then, by utilizing the piecewise sparse property of ITR echo, the HRRP is reconstructed based on compressive sensing (CS). However, the periods of ITR determine the restricted isometry property (RIP) condition and the HRRP reconstruction performance. In order to improve the HRRP reconstruction performance, the ITR period sequence optimization method based on a genetic algorithm (GA) is proposed in this paper. The correlation coefficient of the sensing matrix columns is minimized after optimization so that the RIP of the sensing matrix can be well satisfied. Simulation and comparison results illustrate that the optimization method converges fast and the HRRP reconstruction performance is improved with the optimized ITR periods.

Region-factorized recurrent attentional network with deep clustering for radar HRRP target recognition

Feature extraction plays an essential role in radar automatic target recognition (RATR) with high-resolution range profiles (HRRPs). Traditional feature extraction algorithms usually ignore that different regions in HRRP contain the information with different importance, resulting in their inadequacy in characterizing HRRP data. In this work, we propose a region factorized recurrent attentional network (RFRAN) for HRRP-RATR by making use of the temporal dependence through recurrent neural network (RNN) and automatically finding the informative regions by a deep clustering mechanism in HRRP samples, which reflects the distribution of scatterers in target along range dimension. Specifically, we represent the temporal RNN hidden state using a region factorized encoder whose parameters are conditioned on the HRRP region cluster centers. Moreover an attention mechanism is used to weight up the different recognition contribution of each time step’s hidden state. The aim of all the above modules is to achieve a more informative and discriminative feature. Crucially, the loss function of RFRAN is differentiable, so all components can be jointly trained with a gradient-based optimization. Compared with traditional methods, besides the competitive recognition performance, RFRAN has a promising interpretability thanks to the sequential region-specific hidden states.

Tensor RNN With Bayesian Nonparametric Mixture for Radar HRRP Modeling and Target Recognition

To deal with the temporal dependence between range cells in high resolution range profile (HRRP), dynamic methods, especially recurrent neural network (RNN), have been employed to extract features for target recognition. However, RNN has difficulty in complex and diverse sequence modeling problems as it ignores non-stationary sequential relationship between time-steps by sharing same parameters among all time-steps. Given this issue, we propose tensor recurrent neural network with Gaussian mixture model (GmTRNN) for HRRP, not only making use of temporal characteristic but also modeling the variation among its patterns. Specifically, a novel tensor RNN is developed by extending all the parameters in the form of tensor to explore diverse temporal dependence between range cells within an HRRP sample, where a mixture model is introduced to determine the parameters of each time-step in tensor RNN. Moreover, to take advantage of Bayesian nonparametrics in handling the unknown number of mixture components, we further propose the tensor recurrent neural network with Dirichlet process mixture (DPmTRNN). For scalable and joint training of clustering and recognition, we present effective hybrid online variational inference and stochastic gradient descent method. Experiments on benchmark data, measured and simulated HRRP data demonstrate the the effectiveness and efficiency of our models and its robustness to HRRP shift.

Impact of the Half Space on High Resolution Range Profile

High resolution range profile (HRRP) which contains the characteristics of the scattering centers of the target, has an important application in the field of radar automatic target recognition. Based on the formation principle of HRRP, this paper analyzes the scattering mechanism of targets in half space and the influence of half space environment on HRRP. The half space HRRP of the same object in different viewing angles and relative dielectric constant is obtained by simulation, and it is compared with the HRRP in free space to further verify the correctness of the analysis. The analysis results have certain significance for processing half space HRRPs and half space target recognition.

Deep-Learning for Radar: A Survey

A comprehensive and well-structured review on the application of deep learning (DL) based algorithms, such as convolutional neural networks (CNN) and long-short term memory (LSTM), in radar signal processing is given. The following DL application areas are covered: i) radar waveform and antenna array design; ii) passive or low probability of interception (LPI) radar waveform recognition; iii) automatic target recognition (ATR) based on high range resolution profiles (HRRPs), Doppler signatures, and synthetic aperture radar (SAR) images; and iv) radar jamming/clutter recognition and suppression. Although DL is unanimously praised as the ultimate solution to many bottleneck problems in most of existing works on similar topics, both the positive and the negative sides of stories about DL are checked in this work. Specifically, two limiting factors of the real-life performance of deep neural networks (DNNs), limited training samples and adversarial examples, are thoroughly examined. By investigating the relationship between the DL-based algorithms proposed in various papers and linking them together to form a full picture, this work serves as a valuable source for researchers who are seeking potential research opportunities in this promising research field.