High Resolution Range Profile Research Articles

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.

Time-Domain Scattering Characteristics and Jamming Effectiveness in Corner Reflectors

The outboard corner reflectors are widely used as a countermeasure to radar tracking and detection. Therefore, it is essential to determine the composite time-domain scattering characteristics of the corner reflector and ship against the sea background and the jamming effectiveness of the corner reflector array. In this paper, the composite electromagnetic scattering characteristics of ship target, corner reflector, and the sea surface are studied based on the time-domain bouncing ray method (TDSBR). The radar echo and broadband radar cross section (RCS) results for the time domain pulse incident are also obtained and the high-resolution range profile (HRRP) is calculated. We then devise an evaluation system based on centroid jamming intensity and range profile similarity to determine the jamming characteristics of passive jamming. We further propose an array composing of three sizes of corner reflectors. Finally, the centroid jamming effectiveness is analyzed by comparing the RCS of the corner reflector array and the ship, and the deception jamming effectiveness is measured by calculating the range profile similarity based on the cosine similarity method and the range profile peak envelope overlap. Simulation results show that the reflector array can achieve a good performance both for centroid and range profile deception. The simulation results given and the evaluation method proposed in this paper provides significant practical insights into the design and analysis of the passive jamming systems.

Evidence-Theoretic Reentry Target Classification Using Radar: A Fuzzy Logic Approach

This study focuses on the reentry target classification and fuses target features based on the generalized evidence theory. The features are extensively investigated, and the ballistic factor and length of the high-resolution range profile are selected. The evidence theory is advantageous for solving feature fusion, representing uncertainty, and is widely used in defense applications. However, determining the generalized basic probability assignment (GBPA) and dealing with uncertainty is a matter that requires further improvement. In this paper, we propose a new method to determine GBPA using uncertainty with time-series radar data. First, the samples of each known class are encoded as a generalized fuzzy number (GFN), and the power set comprising the frame of discernment (FOD) is calculated from the GFN and each intersection area. Subsequently, the test samples with uncertainty are encoded as triangular fuzzy numbers, reflecting the mean and standard deviation of a Kalman filter. Finally, the firing strength between the model and the input is calculated as the degree of support for the class hypothesis, which is used to determine the GBPA. The proposed algorithm is compared with the existing methods and exhibits high classification accuracy and a short classification time without leakage. In experiments with various input uncertainties, the results demonstrate that our method can effectively reflect the input uncertainty and determine the GBPA.

Polarimetric HRRP Recognition Based on ConvLSTM With Self-Attention

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. Conventional polarimetric HRRP recognition methods generally extract a set of features and put them into a well-trained classifier, which heavily rely on expertise. On the contrary, deep learning can automatically learn deep features of the training data and has obtained state-of-the-art results in many classification tasks. In this article, a novel deep model based on convolutional long short-term memory (ConvLSTM) network and self-attention mechanism is proposed for polarimetric HRRP recognition. In the proposed model, ConvLSTM employs the convolutional operator and recurrent LSTM structure to capture and aggregate target polarimetric and spatial scattering information from different dimensions simultaneously. Self-attention mechanism is introduced before ConvLSTM to make the model focus on the discriminative range cells and help improve the learning capacity of ConvLSTM. Experiment results on the simulated and measured datasets demonstrate the effectiveness of the proposed model for multi-class targets recognition. The results of two expanded experiments also show that this model performs well in noise and small training sample cases, showcasing good potentials in practical applications.

High-accuracy automatic target recognition scheme based on a photonic analog-to-digital converter and a convolutional neural network.

We propose a high-accuracy automatic target recognition (ATR) scheme based on a photonic analog-to-digital converter (PADC) and a convolutional neural network (CNN). The adoption of the PADC enables wideband signal processing up to several gigahertz, and thus high-resolution range profiles (RPs) are attained. The CNN guarantees high recognition accuracy based on such RPs. With four centimeter-sized objects as targets, the performance of the proposed ATR scheme based on the PADC and CNN is experimentally tested in different range resolution cases. The recognition result reveals that high-range resolution leads to high accuracy of ATR. It is proved that when dealing with centimeter-sized targets, the ATR scheme can acquire a much better recognition accuracy than other RP ATR solutions based on electronic schemes. Analysis results also show the reason why higher recognition accuracy is attained with higher-resolution RPs.

Class factorized complex variational auto-encoder for HRR radar target recognition

In the field of radar automatic target recognition (RATR), the high-resolution range profile (HRRP) has received intensive attention. Bar a few exceptions, almost all HRRP-based ATR classification systems ignore the phase of the HRRP when the data is input to the classifier, relying instead only on the magnitude of the complex HRRP samples. This approach ignores the phase of the complex HRRPs, which reduces the information in the signal. In this paper, we develop a novel class factorized complex variational auto-encoder (CFCVAE) to utilize the phase of the high range resolution (HRR) radar echo for recognition. The CFCVAE is a complex-valued neural network (CV-NN) consisting of the encoding and decoding modules. In CFCVAE, the encoding module projects the observed data into the latent space, and then the latent features are fed to the decoding module, which further maps the latent features to data. In particular, the decoding module introduces the class labels to partition the whole observations into some parts, each of which is depicted by a specific class-decoder. Compared with the traditional variational auto-encoder (VAE) containing a single decoder, the CFCVAE can give a more accurate description to the whole dataset via multiple class-decoders, thus improving the characterization ability of features. In addition, based on the class labels, the CFCVAE employs the conditional prior on the latent variable to enhance the discrimination of features. Moreover, a complex backpropagation algorithm is derived for CFCVAE training, and a sample is classified to the class corresponding to the class-decoder with the minimum reconstruction error in the test stage. Experimental evaluations on the measured data indicate that the proposed method indeed achieves very promising target recognition performance.

Radar group target recognition based on HRRPs and weighted mean shift clustering

When range high-resolution radar is applied to target recognition, it is quite possible for the high-resolution range profiles (HRRPs) of group targets in a beam to overlap, which reduces the target recognition performance of the radar. In this paper, we propose a group target recognition method based on a weighted mean shift (weighted-MS) clustering method. During the training phase, subtarget features are extracted based on the template database, which is established through simulation or data acquisition, and the features are fed to the support vector machine (SVM) classifier to obtain the classifier parameters. In the test phase, the weighted-MS algorithm is exploited to extract the HRRP of each subtarget. Then, the features of the subtarget HRRP are extracted and used as input in the SVM classifier to be recognized. Compared to the traditional group target recognition method, the proposed method has the advantages of requiring only a small amount of computation, setting parameters automatically, and having no requirement for target motion. The experimental results based on the measured data show that the method proposed in this paper has better recognition performance and is more robust against noise than other recognition methods.

Study of Moving Targets Tracking Methods for a Multi-Beam Tracking System in Terahertz Band

This paper studies the methods of moving targets tracking based on a multi-beam tracking system in terahertz (THz) band, which is an initial attempt in this field. Taking the advantage of forming high resolution range profile of THz radar, a target geometric center tracking method is developed at first. However, the tracking performance is deteriorated by the severe angular glint and system disturbance. A further analysis on the experiment data finds a statistical correlation between the angular glint and the normalized amplitude variance, which is exploited to identify and exclude instable scatterers. A non-linear angular discrimination function is also established to deal with the anomalous measurement of the radar. Finally, an instable point exclusion method and a nonlinear angular discrimination method are proposed and combined to develop an improved tracking method, which can suppress angular glint and mitigate measuring perturbation, hence promoting the tracking performance. Proof-of-principle experiments are performed with a multi-beam tracking system working in 0.2 THz band. Tracking results of different targets demonstrate that the improved tracking method can effectively eliminate the tracking failure and significantly promote the tracking accuracy.

A variable chirp rate stepped frequency linear frequency modulation waveform designed to approximate wideband non-linear radar waveforms

The non-linear frequency modulation (NLFM) waveform is one of the existing waveforms that can be used in high range resolution radar applications. However, a high sampling frequency and consequently an expensive ADC are required. To overcome this drawback while taking advantage of the features of the NLFM waveform, we suggest approximating the wideband NLFM waveform by a piecewise linear waveform and using it in a stepped frequency (SF) framework. Thus, a variable chirp rate SF-LFM waveform is proposed where SF is combined with a train of LFM pulses having different chirp rates, durations, and bandwidths. In this paper, these parameters are derived from a tangent-based NLFM waveform. At the receiver, a generalized version of the time domain (TD) algorithm is proposed to process the received echoes. Our purpose is to obtain the high range resolution profile (HRRP) whose properties are of the same magnitude orders as those obtained using a tangent-based NLFM waveform. These properties are the peak sidelobe ratio, the integrated sidelobe, and the range resolution. Toward this goal, a multi-objective optimization issue is addressed to deduce the parameters of the proposed waveform by using two types of approaches based on evolutionary algorithms. Their relevance is compared. Our analysis and simulations show that the proposed approaches attain the targeted performance goals with a smaller sampling frequency at the receiver.

Research on HRRP target recognition based on one-dimensional stack convolutional autoencoder

Extracting key features from the radar high resolution range profile (HRRP) determines the accuracy and reliability of radar target recognition. Aiming at the problem of feature extraction and recognition in HRRP target recognition, a one-dimensional stack convolutional autoencoder (1D-sCAE) recognition method is proposed. Firstly, one-dimensional convolutional autoencoder is constructed to extract the deep features of one-dimensional HRRP signals by unsupervised learning. Then, multiple one-dimensional convolutional autoencoders are stacked to construct 1D-sCAE, and HRRP is classified by fine-tuning the network with label data. At the same time, for the overfitting problem of 1D-sCAE, dropout technology is added to optimize and improve the generalization performance. Through the simulated HRRP data of the target in the middle part of the trajectory, the experiment shows that the proposed algorithm has better feature extraction capabilities, higher recognition accuracy and stronger robustness than the typical deep neural networks.

A staked discriminative auto-encoder based on center loss for radar target HRRP recognition

In the radar automatic target recognition filed, extracting representative features from the radar high resolution range profile (HRRP) is the key issues, which determines the accuracy and reliability of radar target recognition. In this paper, we propose a novel stacked discriminative auto-encoder(S-disAE), the center loss is integrated into auto-encoder, it can force the learned feature with large distance close to their class feature representation center, so as to reduce the intra-class variations while keeping the features of different classes separable. In pre-training stage of our discriminative auto-encoder(disAE), we combine the mean square error loss and center loss to learn the main factors of input raw data and minimize the intra-class variations of the feature, in fine-tuning stage, we also combine the softmax cross entropy loss and center loss to improve the classification accuracy. We conduct several experiments on the simulated HRRP data, the results demonstrate that the proposed algorithm can extract discriminant features and improve the HRRP recognition accuracy.

Microwave Photonic Wideband Distributed Coherent Aperture Radar With High Robustness to Time Synchronization Error

In this article, we firstly analyze the specific effect of time and phase synchronization errors on the coherent gain of microwave photonic wideband distributed coherent aperture radar (DCAR), and further propose a robust design based on bandwidth segmentation. In our design, the intermediate-frequency narrowband linear frequency modulated waveform (LFMW) sub-pulses are mixed with the stepped-frequency local oscillator signals at the transmitter, generating the stepped-frequency LFMW. To ensure the waveform purity, a photonics-based single-sideband up-converter is proposed as the mixer. At the receiver side, the echo wave is processed by a photonics-based down-converter combined with the matched-filtering processing. The relatively low-resolution range profiles with low coherent gain loss can be achieved by the narrowband sub-pulses, and the high-resolution range profile with low coherent gain loss can be synthesized from these low-resolution range profiles. The coherent gain of the proposed system is less sensitive to time synchronization error comparing to the system where the wideband LFMW is transmitted. Higher level of robustness also enables easier implementation of such system. To show the effectiveness of our design, we conduct simulations and further establish an X-band (8–12 GHz) microwave photonic system with two transmitters and one receiver. The coherent gain under different time synchronization errors are measured. Both the simulation and experimental results confirm that the proposed system achieves a higher robustness to time synchronization error. For instance, to keep the coherent gain loss less than 0.3 dB, the required time synchronization accuracy can be relaxed from 35 ps to 127 ps.

Theoretical and Experimental Analysis of Radar Micro-Doppler Signature Modulated by Rotating Blades of Drones

Radar signatures contributed by rotating blades of drones usually refer to the kinematical micro-Doppler phenomenon on spectrograms. Here, theoretical analysis indicates that when sampling time is short enough that this signature becomes instant micro-Doppler signatures with scattering characteristics, appearing as “blade flash” in the time domain and rotor blade modulation (RBM) feature in the spectrum. In an anechoic chamber, we track this instant micro-Doppler signature by using a dynamic measurement system composed of a network analyzer and a high-speed camera to collect high-resolution range profile of a consumer drone, DJI Phantom 3 Vision within S-band, X-band, and Ku-band. The experimental results demonstrate that rotating blades do cause signal fluctuation of “blade flash” and register the RBM feature in the spectrum, and the modulation effects are weak within S-band, strong within X-band, and stronger within Ku-band. This instant micro-Doppler with scattering characteristic establishes a mapping between radar signature and the rotation structure component of the drone and enables us to distinguish a drone from other nonblades objects in the air, especially with little radar observation time.

협대역 레이다 시스템에서 표적의 고해상도 거리 측면도 형성에 관한 연구

본 논문에서는 협대역 레이다 시스템에서 표적의 고해상도 거리 측면도를 형성하고, 표적의 산란점 구별이 용이하도록 하는 품질 향상 기법을 제안하였다. Sparse한 주파수 환경에서 획득된 데이터를 시간 영역 상관(Time-Domain Correlation) 방법을 이용하여 표적의 고해상도 거리 측면도가 획득된다는 것을 확인하였다. 또한 제한된 거리 쉘 내에서 여러 개의 산란점이 존재하는 표적의 경우 산란점 구별이 용이하지 않았다. 이를 개선하기 위하여 획득된 데이터를 선별적으로 제외하여 거리 측면도를 형성한 후 고해상도 거리 쉘 별로 최소 값을 선택하는 방법을 제안하였으며, 시뮬레이션을 통해 제안 방법으로 해상도 및 부엽준위가 향상된 거리 측면도를 획득할 수 있음을 확인하였다.

Eigenvalue‐based ground target detection in high‐resolution range profiles

In this study, the authors address the problem of range-distributed target detection in sequential high resolution range profiles (HRRPs). They propose a modified scaled largest eigenvalue detector for static target in homogenous ground clutter. A set of secondary data, which are free of target signal and have the same distribution as the clutter in the primary data, are assumed to be available. First, the sample covariance matrix (SCM) is estimated from the acquired multiple HRRPs in a short coherent processing interval. Then, the eigenvalue decomposition of the SCM is performed, and the eigenvalues are sorted in descending order. Finally, the largest eigenvalue scaled by the noise power estimated from the secondary data is selected as the detection statistic. Compared with existing methods of largest eigenvalue-based detection, the proposed method achieves better detection performance for coloured clutter by considering secondary data. Numerical and experimental results demonstrate the effectiveness of the proposed method.