High Resolution Range Profile Research Articles

TOPSIS missile target selection method supported by the posterior probability of target recognition

Abstract Addressing the problem of being easily interfered by false targets such as chaff and corner reflector when selecting missile targets, a multi-feature multi-model target selection method based on the technique for order preference by similarity to ideal solution (TOPSIS) is proposed if only single-target characteristics such as target position or radar cross section (RCS) size is used in the selection of missile targets. The target selection problem under the comprehensive utilisation of multiple features is regarded as a problem in decision-making based on multiple attributes. The BP network based on the target RCS frequency domain statistical features, the radial basis network based on the target polarisation feature and the radial size feature based on high-resolution range profile (HRRP) are realised. The comprehensive utilisation of C-support vector machine (SVM) and other target recognition methods and the comprehensive sorting of target selection, to a certain extent, enable anti-ship missiles to make the correct target selection more accurately. The simulation results display that the effective selection of ship targets can be achieved in the case of passive interference by surface ships.

표적 탐지 및 식별 성능 향상을 위한 레이더 측정치 분석

Using artificial intelligence, radar measurements can improve the performance of target detection and identification methods. Testing data was generated from existing radar data, high-resolution range profile, and inverse synthetic aperture radar images. Radar cross section was chosen as the main parameter because it has been widely studied and its parameters are well known. To ensure the quality of the training dataset, we used simulation tools to determine which parameters had the greatest effect on the generated data. Missile models with different shapes were applied during simulations to confirm the possibility of detection and identification when suitable parameters were applied.

Aerial target classification using an LFL-Net with multiview HRRPs

ABSTRACT Non-cooperative aerial target classification is one of the most attractive but challenging tasks in radar remote sensing applications. Multiview high-range resolution profiles (HRRPs) of the aerial target contain abundant information and will benefit to classification. In this paper, a new aerial target classification method based on an end-to-end lightweight feature learning network (LFL-Net) with multiview HRRPs is proposed. The aerial target classification scenario using multiview HRRPs is first studied and modelled. Then a LFL-Net with multi-inputs and some distinct modules is designed to effectively learn the target classification information from the multiview HRRPs. Therefore, the proposed method can achieve accurate and reliable classification results under different signal-to-noise ratios (SNRs). Experimental results have shown the superiorities of the proposed non-cooperative aerial target classification method.

Sparse processing with range migration compensation capability for a frequency agile orthogonal frequency division multiplexing waveform

Digital-to-analogue and analogue-to-digital in a digital array radar limits the transmission and reception of instantaneous broadband signals (IBSs), which makes it a technical bottleneck for digital array radar to obtain an instantaneous broadband high-resolution range profile (HRRP). A frequency-agile orthogonal frequency division multiplexing (OFDM) waveform with a high repetition frequency pulse train is proposed, which uses multiple narrowband OFDM to realise large bandwidth so that the digital array radar can easily realise the broadband working mode. This frequency hopping signal makes the digital array radar have the characteristics of broadband HRRP formation and low interception simultaneously. To solve the problem of range migration caused by high-speed moving targets, a two-stage sparse processing algorithm with range migration compensation capability (FS-RMCSP) in slow time and fast time domains is proposed. By constructing a sparse processing dictionary matrix with range compensation operator according to the phase offset of the moving target echo, the proposed algorithm can complete the compensation of target range migration with high resolution, accurately recover the range-Doppler parameters of target, and realise the coherent accumulation of target echo. The simulation results show that this waveform can obtain the range-Doppler high-resolution characteristics equivalent to the IBS in the radar tracking working mode. Compared with the conventional sparse signal processing algorithm, the FS-RMCSP algorithm significantly improves the performance of target reconstruction in the scene of high-speed moving targets.

Target-attentional CNN for Radar Automatic Target Recognition with HRRP

In this paper, a target-attentional convolutional neural network (TACNN) combining the convolutional neural network (CNN) and attention mechanism is proposed for radar high-resolution range profile (HRRP) target recognition. The TACNN takes one-dimensional CNN (1-D CNN) as the feature extractor and has the capability to excavate abundant local structural features of data. However, the HRRP contains non-target areas, where the information is useless or even unfavorable. Furthermore, different parts of HRRP target regions should have differences in contribution to the recognition task. Therefore, it is an inadvisable approach that treats all local features alike and directly uses them for the subsequent target recognition, which is adopted by a lot of models, such as the conventional CNN. To tackle this problem, the TACNN introduces the attention mechanism on the basis of 1-D CNN. In detail, the constructed attention module adaptively assigns a weight to each local feature of HRRP so as to locate the target areas and meanwhile enhance the interest of model in valuable target information. Specially, the attention mechanism in TACNN is realized via a bidirectional gated recurrent unit (Bi-GRU) network, where the attention coefficients used for weighting up local features are generated with full consideration of sequential relationship among different regional features in HRRP. Therefore, the learned attention coefficients in our TACNN can better represent the importance of each local feature to the recognition task, ultimately beneficial for the discovery of target information with more discriminability. Experimental results on measured HRRP data show that the proposed model can get more effectiveness in target recognition than related methods.

Use of ICA to Separate Micro-Doppler Signatures in ISAR Images of Aircraft That Has Fast-Rotating Parts

In this article, we propose a new signal-decomposition algorithm composed of three steps: principal components analysis to yield high-resolution range profiles with improved signal-to-noise ratio; estimation of whitening and mixing matrices using independent component analysis in distributed radar network; and signal decomposition to obtain inverse synthetic aperture radar (ISAR) images that correspond to rigid body and fast-rotating parts by using estimated two matrices regardless of complicated range migration. The proposed method efficiently removes image blur caused by micro-Doppler (MD) signals of rotating parts and reduces the sensitivity to noise. In simulations, our proposed method could perform accurate and robust removal of the MD signatures to obtain a focused ISAR image of an aircraft with fast-rotating parts.

Multi-View HRRP Generation With Aspect-Directed Attention GAN

In radar automatic target recognition (RATR), high-resolution range profile (HRRP) has received intensive attention due to its low computational cost. As HRRP is sensitive to the aspect of the target, a training set covering sufficient aspects is essential to the success of a RATR model, which is however intractable in complex environment with noncooperative targets. In this paper, an aspect-directed attention generative adversarial network (ADA-GAN) is proposed to generate multi-view HRRPs using real samples from few aspects. The key is that the HRRPs from the similar targets share the same aspect variation pattern. Hence, an HRRP is decomposed into its identity and aspect features via an aspect-directed disentangled representation network with self-attention modules. In the training stage, the decomposition network and the aspect variation pattern are learned from full aspect samples of cooperative targets. When generation, the desired multi-view HRRPs of the noncooperative target are synthesized by its identity features extracted from few aspect samples and the learned aspect variation pattern. Three types of experiments on the simulated and measured datasets demonstrate the generation performances of our method. First, the generated HRRPs are visually compared with the truth. Second, the similarity of the scattering center power and handcrafted feature distributions are quantitatively evaluated. Finally, recognition experiments verify the feasibility of data augmentation with the generated HRRPs. Extensive results show the superior performance of our method over other state-of-the-art methods.

Radar Pulse Signal Design and Imaging Method Based on Coded Modulation

Radar faces various interferences from the reconnaissance and other devices when conducting target imaging and recognition. The properties of traditional modulation methods, such as linear frequency modulation (LFM) and phase code modulation (PCM), have been investigated in detail. Therefore, it can be intercepted and recognized easily so that the target image is difficult to be obtained. To improve the complexity of radar signals, the pulse signal design and imaging method is proposed in this paper based on coded modulation. Taking LFM signal as an example, it is modulated by the code sequences firstly. As the spectrum of LFM signal is aliasing after coded modulation, the false peaks appear in target high resolution range profile (HRRP). To eliminate the false peaks in HRRP, the plus-minus coded modulation is designed according to the duty ratio of the first coded modulation signal. Then, the HRRP can be recovered for target imaging. The whole procedure of the coded modulation and imaging is provided and analyzed. Besides, based on different coded modulation signals, the entropy of signal time-frequency image is discussed to illustrate the anti-jamming performance. Simulations and experiments are conducted to verify the effectiveness of the proposed signal design and imaging method.

A Practical Deceptive Jamming Method Based on Vulnerable Location Awareness Adversarial Attack for Radar HRRP Target Recognition

In recent years, deep neural networks are increasingly popular in the field of radar high-resolution range profiles (HRRPs) target recognition. Unfortunately, recent researches have revealed that a deep-learning classifier can be easily fooled by adding small perturbations to the input, named adversarial attack. This provides us an inspiration for radar deceptive jamming signal generation in electronic countermeasures (ECMs). However, the perturbations generated by these adversarial attacks are usually of complex envelopes and quite low power, making it challenging for jammers to generate such actual jamming signals. To solve that issue, we propose a practical deceptive jamming generation method that learns the vulnerable range cells in an HRRP sample and injects several jamming pulses with specific amplitudes into these range cells. Such jamming signals are easy to generate and can deceive the radar automatic target recognition (RATR) model to output the wrong target category prediction with high confidence. To avoid the requirement of the recognition network structure information, we leverage the differential evolution optimization algorithm (non-gradientbased). Further, to provide the potential of real-time jamming signal generation during the test, an encoder is constructed not only to learn the separable features but also to find the vulnerable range cells and the specific amplitudes of the jamming pulses. In the experiments, we apply the proposed attack algorithms to fool the one-dimensional convolutional neural network-based HRRPRATR models. The extensive experimental results on measured aircraft HRRP dataset prove that the proposed algorithms achieve a promising attack performance and serve as a practical and fast deceptive jamming generation method.

Radar HRRP Target Recognition Method Based on Multi-Input Convolutional Gated Recurrent Unit With Cascaded Feature Fusion

Over the past decades, radar high-resolution range profile (HRRP) has been one of the research highlights in the field of radar automatic target recognition (RATR) due to its advantages of easy acquisition, small amount of data, and rich target structure information. However, most of existing methods only consider its amplitude (time domain) characteristics, thereby neglecting the temporal dependence and multi-domain features inside the HRRP sequence. To this end, we propose an end-to-end multi-input convolutional gated recurrent unit neural network, called MIConvGRU, for RATR by both exploiting the multi-domain and temporal information to improve the recognition performance of HRRP target. Initially, the data-preprocessing module is employed to extract the multi-domain features of the target, including time domain, frequency domain, and time-frequency domain features, in order to further enhance the target representation. In addition, a cascaded multi-input GRU structure is designed to acquire the multi-domain temporal dependence feature of HRRP sequence from low to high level. Finally, these temporal features are adaptively fused by a parameter learnable strategy. The experimental results show that the proposed MIConvGRU can effectively learn the multi-domain temporal dependence correlation features in HRRP sequences, improving the target recognition performance.

One-Shot HRRP Generation for Radar Target Recognition

Insufficient data of a noncooperative target seriously affect the performance of radar automatic target recognition (RATR) using the high-resolution range profile (HRRP), especially when the noncooperative target has only one sample. To this end, we propose an unsupervised data generation method to generate noncooperative HRRP signals. We utilize the pretrained generative adversarial networks (GANs) model to learn the HRRP general probability distribution. To emphasize the representative and discriminative power of generated HRRP signals, a joint optimization method is proposed to preserve category information. Moreover, a feature diversification method is proposed to make the generated samples have sufficient aspect characteristics to further fit the probability distribution of the noncooperative target. Thus, the generated HRRP signals can effectively improve the recognition performance of noncooperative target. Extensive experiments on HRRP data sets demonstrate the superior performance of our method over other state-of-the-art methods.

Radar Target Recognition by Convolutional Capsule Networks Based on High-Resolution Range Profile

Automatic target recognition (ATR) is of increasing importance for the modern radar system, where the high-resolution range profile (HRRP) is essential. However, the recognition accuracy and sensitivity to dataset should be optimized for practical applications. Herein, we proposed a novel algorithm for HRRP target recognition based on a convolutional capsule network rather than neurons in conventional deep neural networks. The capsules were vectors trained to learn latent features from input HRRP, with the length of the vector representing the confidential probability. The convolution dynamic routing mechanism was applied between capsule layers by shared transformation matrices and constrained routing procedures in local kernels, reducing the size of parameters and computational expense. Experiments on measured data proved that the proposed algorithm outperforms other existing methods with higher recognition accuracy and less sensitivity to training size. This study provided a promising and effective approach for HRRP target recognition.

Research on the target recognition of marine surveillance radar based on ensemble learning

The existence of corner reflector decoy makes the marine surveillance radar to be caught in severe challenges of target recognition. In order to enhance the accuracy of recognition, an ensemble classifier for marine targets is created based on the self-built dataset of high resolution range profile (HRRP). In addition, a confidence evaluation algorithm based on non-parametric estimation of probability density is proposed to reject unknown decoy target outside the database. With taking different interference conditions into consideration respectively, the comparison experiment between ensemble classifier and single classifier is carried out. The results show that the ensemble classifier is significantly better, which has strong robustness to noise as well as rejection ability to unknown target, and the classification accuracy can reach 92.63% under ideal conditions. This paper proves the feasibility of ensemble learning for maritime target recognition, and provides a reliable classification algorithm when the sample information is sufficient.

Adaptive Double Threshold Detection Method for Range-Spread Targets

The performance of wideband radar detectors can be further improved if the features of a target's high-resolution range profile (HRRP) are fully utilized. In this paper, a target adaptive double threshold detection method is proposed. First, the amplitude distribution parameters of the HRRP's range cells are extracted for data generation, and the detection probability is statistically calculated. The online gradient descent method is then used to adaptively adjust the second thresholds of the double threshold detector, thereby improving the detection performance with a given constant false alarm rate. The effectiveness of the proposed algorithm is verified using two sets of measured data. The proposed algorithm exhibits superior detection performance compared to traditional detection algorithms for range-spread targets.

Wideband Interference Time–Frequency Feature Prediction and Its Application to Cognitive Radar HRRP Estimation

Wideband interference (WBI) is detrimental to high-resolution radar due to its high power and wide frequency occupancy. In this study, a deep learning (DL) method is proposed to predict the time–frequency (TF) feature of WBI and applied to cognitive radar high-resolution range profile (HRRP) estimation. Specifically, by performing short-time Fourier transform (STFT) on the WBI signal collected in the past and using a sliding window, a series of WBI TF figures is generated. A long short-time memory (LSTM) network is then used to learn the spatiotemporal (ST) correlation of these TF figures, thus predicting the WBI TF feature in the future, based on which, a cognitive method is used for target HRRP estimation with reduced influences of WBI. Numerical results demonstrate the effectiveness of the proposed methods.

ISAR Imaging Analysis of a Hypersonic Vehicle Covered With Plasma Sheath

In this article, a hypersonic target electromagnetic (EM) scattering echo model combined with the inhomogeneous zonal medium model (IZMM) and the classical scattering center model (SCM) is proposed with a distributed satelliteborne array radar as the detection platform. A parallel physical optics (PO) method is used for multiview inverse synthetic aperture radar (ISAR) imaging of a moving hypersonic target covered with plasma sheath based on the analysis of high-resolution range profile in the S-X ultrawideband range, reconstructing 2-D EM scattering echo data (the target) and motion compensation. The results show that the surface of the inhomogeneous plasma sheath flow field is an excitation layer with random and irregular fluctuation characteristics, which increases the false scattering centroid of the 1-D range profile of the hypersonic target and can interfere with and disrupt the radar localization of the target along the radial direction. In addition, shallow scattering of EM waves occurs in the plasma sheath, and the average signal intensity of the target can gradually reduce from 0.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times \,\,10^{-5}$ </tex-math></inline-formula> at 60 km and 20 Ma to 0.1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times \,\,10^{-5}$ </tex-math></inline-formula> at 30 km and 20 Ma, with a fivefold weakening of the overall scattered echo signal. In particular, the faster the hypersonic target travels at 30-km altitude, the weaker the imaged scattered echo signal becomes, with the average intensity of the imaged signal weakening by approximately threefold from 15 to 25 Ma. This study provides considerable technical support and data assurance to establish a synthetic aperture radar (SAR) automatic target recognition (ATR) database, and the findings of this study can be used as a reference for fine-structure feature analysis of hypersonic targets for feature extraction and the classification and identification of targets.

Radar High-Resolution Range Profile Target Recognition by the Dual Parallel Sequence Network Model

Using traditional neural network algorithms to adapt to high-resolution range profile (HRRP) target recognition is a complex problem in the current radar target recognition field. Under the premise of in-depth analysis of the long short-term memory (LSTM) network structure and algorithm, this study uses an attention model to extract data from the sequence. We build a dual parallel sequence network model for rapid classification and recognition and to effectively improve the initial LSTM network structure while reducing network layers. Through demonstration by designing control experiments, the target recognition performance of HRRP is demonstrated. The experimental results show that the bidirectional long short-term memory (BiLSTM) algorithm has obvious advantages over the template matching method and initial LSTM networks. The improved BiLSTM algorithm proposed in this study has significantly improved the radar HRRP target recognition accuracy, which enhanced the effectiveness of the improved algorithm.

Open set HRRP recognition with few samples based on multi-modality prototypical networks

• We propose an multi-modality prototypical network for open set HRRP recognition. • Our method combines adaptive feature extractor (AFE) and Mahalanobis-based classifier (MC). • AFE and MC are optimized in an end-to-end supervised manner. • Our method can distinguish targets from both seen and unseen targets quickly at test time. • Experimental results show our method performs better than baseline methods. Learning from a few high-resolution range profiles (HRRPs) with multi-modality property caused by sensitivity of the target aspect remains a challenge in radar HRRP-based target recognition. Despite recent advances in HRRP recognition based on deep neural networks (DNNs), thanks to their powerful expressive ability, the standard supervised deep learning paradigm does not offer a satisfactory solution for learning new classes rapidly from a few HRRPs. In this work, we employ ideas from metric learning based on discriminative neural features and from recent advances that augment neural networks with FiLM layers to adapt to multi-modality input data. We first define open set learning problem on HRRP recognition task. Then, we propose a multi-modality prototypical network (MMPN) to attack the problem. Our framework learns a modality-aware network that maps a few labelled HRRPs and unlabelled HRRPs to their labels via a well defined metric space with episodic-based meta-learning strategy, obviating the need for fine-tuning to adapt to new classes. Finally, a synthetic HRRP data, called RareHRRP, is developed to evaluate that the proposed model generalize well and is efficient in computation.

Motion Parameter Estimation and HRRP Construction for High-Speed Weak Targets Based on Modified GRFT for Synthetic-Wideband Radar With PRF Jittering

The synthetic-wideband and pulse repetition frequency jittering radar systems are receiving increasing attention due to the relatively low construction costs and good anti-jamming performance. However, the inter-pulse random hopping of the carrier frequency and pulse repetition frequency introduces challenges to the long-time coherent integration and motion parameter estimation of the high-speed weak targets for these radar systems. A modified generalized Radon Fourier transform for weak target detection and motion parameter estimation is proposed. The proposed method can achieve full coherent integration gain for the high-speed weak target and has a good parameter estimation accuracy. After the target motion has been well compensated, the well-focused high-resolution range profile of the target can be obtained by synthetic-wideband processing. The effectiveness of the proposed method is verified by Monte Carlo simulations. In addition, experimental data collected using an X-band radar system are processed and analyzed to illustrate the practical performance of the proposed method.

Two-Stream Deep Fusion Network Based on VAE and CNN for Synthetic Aperture Radar Target Recognition

Usually radar target recognition methods only use a single type of high-resolution radar signal, e.g., high-resolution range profile (HRRP) or synthetic aperture radar (SAR) images. In fact, in the SAR imaging procedure, we can simultaneously obtain both the HRRP data and the corresponding SAR image, as the information contained within them is not exactly the same. Although the information contained in the HRRP data and the SAR image are not exactly the same, both are important for radar target recognition. Therefore, in this paper, we propose a novel end-to-end two stream fusion network to make full use of the different characteristics obtained from modeling HRRP data and SAR images, respectively, for SAR target recognition. The proposed fusion network contains two separated streams in the feature extraction stage, one of which takes advantage of a variational auto-encoder (VAE) network to acquire the latent probabilistic distribution characteristic from the HRRP data, and the other uses a lightweight convolutional neural network, LightNet, to extract the 2D visual structure characteristics based on SAR images. Following the feature extraction stage, a fusion module is utilized to integrate the latent probabilistic distribution characteristic and the structure characteristic for the reflecting target information more comprehensively and sufficiently. The main contribution of the proposed method consists of two parts: (1) different characteristics from the HRRP data and the SAR image can be used effectively for SAR target recognition, and (2) an attention weight vector is used in the fusion module to adaptively integrate the different characteristics from the two sub-networks. The experimental results of our method on the HRRP data and SAR images of the MSTAR and civilian vehicle datasets obtained improvements of at least 0.96 and 2.16%, respectively, on recognition rates, compared with current SAR target recognition methods.