High-resolution Range Profile Target Recognition Research Articles

Adaptive soft threshold transformer for radar high‐resolution range profile target recognition

AbstractRadar High‐Resolution Range Profile (HRRP) has great potential for target recognition because it can provide target structural information. Existing work commonly applies deep learning to extract deep features from HRRPs and achieve impressive recognition performance. However, most approaches are unable to distinguish between the target and non‐target regions in the feature extraction process and do not fully consider the impact of background noise, which is harmful to recognition, especially at low signal‐to‐noise ratios (SNR). To tackle these problems, the authors propose a radar HRRP target recognition framework termed Adaptive Soft Threshold Transformer (ASTT), which is composed of a patch embedding (PE) layer, ASTT blocks, and Discrete Wavelet Patch Merging (DWPM) layers. Given the limited semantic information of individual range cells, the PE layer integrates nearby isolated range cells into semantically explicit target structure patches. Thanks to its convolutional layer and attention mechanism, the ASTT blocks assign a weight to each patch to locate the target areas in the HRRP while capturing local features and constructing sequence correlations. Moreover, the ASTT block efficiently filters noise features in combination with a soft threshold function to further enhance the recognition performance at low SNR, where the threshold is adaptively determined. Utilising the reversibility of the discrete wavelet transform, the DWPM layer efficiently eliminates the loss of valuable information during the pooling process. Experiments based on simulated and measured datasets show that the proposed method has excellent target recognition performance, noise robustness, and small‐scale range shift robustness.

CVPCNN: Conditionally variational parameterized convolution neural network for HRRP target recognition with imperfect side information

Radar target recognition tasks often incorporate valuable side information (SI) that aids in the recognition task. The effectiveness of SI has been fully verified in traditional statistical modeling approaches. Its full potential remains untapped in deep learning-based high-resolution range profile (HRRP) target recognition tasks. Taking into account the azimuth sensitivity of HRRP, azimuth is chosen as the SI of the proposed model. To integrate SI into deep neural networks, we propose two novel parameterized convolution methods, namely variational parameterized convolution (VPCONV) and the extended conditionally VPCONV (CVPCONV). Specifically, VPCONV makes the convolutional kernel weights as the function of the auxiliary azimuth, which gives the network the capability of adaptively adjusting to changes in azimuth. Acknowledging the challenge of estimating HRRP azimuth accurately in practical scenarios, VPCONV employs a heteroscedastic Gaussian distribution, featuring varying mean and variance, to generalize the estimation error of SI through variational encoding. Moreover, CVPCONV incorporates the properties of the HRRP samples themselves by kernel attention module. Finally, we present a lightweight SI-based network. The experimental results based on the measured HRRPs validate the effectiveness of proposed method across various extended recognition tasks, underscoring the potential of fusing SI via parameterized convolution in advancing target recognition systems.

Dual-band polarimetric HRRP recognition via a brain-inspired multi-channel fusion feature extraction network.

Radar high-resolution range profile (HRRP) provides geometric and structural information of target, which is important for radar automatic target recognition (RATR). However, due to the limited information dimension of HRRP, achieving accurate target recognition is challenging in applications. In recent years, with the rapid development of radar components and signal processing technology, the acquisition and use of target multi-frequency and polarization scattering information has become a significant way to improve target recognition performance. Meanwhile, deep learning inspired by the human brain has shown great promise in pattern recognition applications. In this paper, a Multi-channel Fusion Feature Extraction Network (MFFE-Net) inspired by the human brain is proposed for dual-band polarimetric HRRP, aiming at addressing the challenges faced in HRRP target recognition. In the proposed network, inspired by the human brain's multi-dimensional information interaction, the similarity and difference features of dual-frequency HRRP are first extracted to realize the interactive fusion of frequency features. Then, inspired by the human brain's selective attention mechanism, the interactive weights are obtained for multi-polarization features and multi-scale representation, enabling feature aggregation and multi-scale fusion. Finally, inspired by the human brain's hierarchical learning mechanism, the layer-by-layer feature extraction and fusion with residual connections are designed to enhance the separability of features. Experiments on simulated and measured datasets verify the accurate recognition capability of MFFE-Net, and ablative studies are conducted to confirm the effectiveness of components of network for recognition.

Continuous Learning Method of Radar HRRP Based on CVAE-GAN

To improve the catastrophic forgetting that existed in the high-resolution range profile (HRRP) target recognition model of offline training, this paper proposed a continuous learning method of radar HRRP based on conditional variational auto-encoding and generative adversarial network (CVAE-GAN). Firstly, this method generates data through CVAE to simulate the real training data, and replays it in the series of subsequent tasks. By using generators, the proposed method does not need to save the original HRRP data and can ensure the privacy of the training dataset. Secondly, the proposed method takes the categorical data label as a generating condition, solves the unbalanced categories of the generated data samples, and improves the accuracy of the radar HRRP target recognition. Finally, by combining the attention mechanism and GAN network of the transformer, the generative capacity of the CVAE generator is enhanced effectively. In this paper, the continuous learning method is verified by a validation framework with three task settings. The experimental results show that the proposed method has a better continuous learning ability and better engineering practicality in various tasks compared with the previous regularization methods which can guarantee the privacy of the training dataset.

End-to-End Radar HRRP Target Recognition Based on Integrated Denoising and Recognition Network

For high-resolution range profile (HRRP) radar target recognition in a low signal-to-noise ratio (SNR) scenario, traditional methods frequently perform denoising and recognition separately. In addition, they assume equivalent contributions of the target and the noise regions during feature extraction and fail to capture the global dependency. To tackle these issues, an integrated denoising and recognition network, namely, IDR-Net, is proposed. The IDR-Net achieves denoising through the denoising module after adversarial training, and learns the global relationship of the generated HRRP sequence using the attention-augmented temporal encoder. Furthermore, a hybrid loss is proposed to integrate the denoising module and the recognition module, which enables end-to-end training, reduces the information loss during denoising, and boosts the recognition performance. The experimental results on the measured HRRPs of three types of aircraft demonstrate that IDR-Net obtains higher recognition accuracy and more robustness to noise than traditional methods.

Lightweight Transformer Network for Ship HRRP Target Recognition

The traditional High-Resolution Range Profile (HRRP) target recognition method has difficulty automatically extracting target deep features, and has low recognition accuracy under low training samples. To solve these problems, a ship recognition method is proposed based on the lightweight Transformer model. The model enhances the representation of key features by embedding Recurrent Neural Networks (RNN) into Transformer’s encoder. The Group Linear Transformations (GLTs) are introduced into Transformer to reduce the number of parameters in the model, and stable features are extracted through linear intergroup dimensional transformations. The adaptive gradient clipping algorithm is combined with the Stochastic Gradient Descent (SGD) optimizer to allow the gradient to change dynamically with the training process and to improve the training speed and generalization ability of the model. Experimental results on the simulated dataset show that multi-layer model stacking can effectively extract deep features of targets and raise recognition accuracy. At the same time, the lightweight Transformer model can maintain good recognition performance with low parameters and low training samples.

Intelligent radar HRRP target recognition based on CNN-BERT model

Stable and reliable feature extraction is crucial for radar high-resolution range profile (HRRP) target recognition. Owing to the complex structure of HRRP data, existing feature extraction methods fail to achieve satisfactory performance. This study proposes a new deep learning model named convolutional neural network–bidirectional encoder representations from transformers (CNN-BERT), using the spatio–temporal structure embedded in HRRP for target recognition. The convolutional token embedding module characterizes the local spatial structure of the target and generates the sequence features by token embedding. The BERT module captures the long-term temporal dependence among range cells within HRRP through the multi-head self-attention mechanism. Furthermore, a novel cost function that simultaneously considers the recognition and rejection ability is designed. Extensive experiments on measured HRRP data reveal the superior performance of the proposed model.

Multi‐scale group‐fusion convolutional neural network for high‐resolution range profile target recognition

Convolution neural networks (CNNs) represent one of the workhorses of artificial intelligence applications. As a typical artificial intelligence application, a high-resolution range profile (HRRP) target recognition method based on CNNs has aroused a lot of research interest. Most CNNs use a relatively small and single-scale convolution kernel size to control the number of parameters and computational complexity, but recent studies indicate that CNNs with a small kernel size cannot extract enough spatial information, which hurts the recognition performance. Aiming at this problem, this paper proposes a multi-scale group-fusion one-dimensional convolution neural network (MSGF-1D-CNN) for HRRP target recognition. MSGF-1D-CNN utilises multi-scale group one-dimensional convolution (MSG 1D-Conv) and point-wise convolution (PW-Conv) to replace the standard convolution. Multi-scale group one-dimensional convolution can significantly reduce complexity and capture the information of targets within HRRP in different levels of detail to enhance feature extraction, while PW-Conv can realise the fusion of multi-scale features to help boosting recognition performance. Experiments on five mid-course ballistic targets in the HRRP dataset show that MSGF-1D-CNN has superior recognition performance, and the parameter number of the model is reduced by more than 2.4 times than standard 1D-CNN. Furthermore, MSGF-1D-CNN shows better performance on fine-grained HRRP target recognition and anti-noise robustness in most cases.

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.

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.

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.

A novel radar target recognition method for open and imbalanced high-resolution range profile

High-resolution range profile (HRRP) data often has imbalanced and open-ended distribution in realistic radar target recognition task, requiring the recognition system classify targets among majority and minority classes and detect unseen targets accurately. In this paper, we propose a novel algorithm for open and imbalanced HRPP recognition tasks, by learning from realistic data distribution and optimizing the accuracy over the majority, minority and open classes. This method maps the HRRP data to the latent feature space, enhances the feature by sharing knowledge from majority to minority class, and provides a scalar indicating the familiarity to known classes. Dual-attention is developed to provide strong discriminative feature representation. An angular penalty is employed in the loss function to optimize the intra-class similarity and inter-class variability. Experiments on measured data prove that the proposed algorithm outperforms other existing methods under both close and open settings with accuracy increased significantly, and more discriminative representation. This study provides a promising and effective approach for open and imbalanced HRRP target recognition.

Bidirectional recurrent gamma belief network for HRRP target recognition

To characterize the bidirectional temporal dependence across the range cell of high resolution range profile (HRRP), we propose a bidirectional deep Poisson gamma dynamical system (bi-DPGDS) to extract features for target recognition. The proposed bi-DPGDS is a bidirectional dynamical deep probabilistic generative model, which builds temporal deep structure with a hierarchy of gamma distributions. For scalable training and fast out-of-sample prediction, we generalize bi-DPGDS to bidirectional recurrent gamma belief network (bi-rGBN) by incorporating a bidirectional recurrent variational inference network, which incorporates the temporal correlations from both directions into latent representation, and introduce a hybrid Bayesian inference scheme combining stochastic gradient Markov chain Monte Carlo (SG-MCMC) and amoritized variational inference. Moreover, we further propose an attention bi-rGBN (attn-bi-rGBN) for supervised learning. Experimental results on measured HRRP data demonstrate the effectiveness and efficiency of our model in classification and generalization, and its robustness to HRRP shift and data size.

Domain-aware meta network for radar HRRP target recognition with missing aspects

Conventional high resolution range profile (HRRP) based radar automatic target recognition (RATR) methods are based on a complete template library assumption. However, such an assumption can barely hold in real world applications since it is difficult to observe target echoes from all aspects, especially for noncooperative targets. To alleviate the target-aspect missing problem, we develop a domain-aware meta network (DOAMN) for HRRP-based RATR. Specifically, the DOAMN first uses a domain-aware (DOM) module to distinguish whether HRRPs come from the seen or unseen target aspects, then a meta network (MNet) is employed to learn a generalized parameter initialization that is able to achieve fast adaptation among target aspects. To enable the proposed DOAMN to be trained in an end-to-end manner, we further present an effective iterative hybrid optimization method. Experiments on simulated HRRP dataset demonstrate the effectiveness and efficiency of the proposed model.

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.

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.

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.

Adversarial attacks on deep-learning-based radar range profile target recognition

Target recognition based on a high-resolution range profile (HRRP) has always been a research hotspot in the radar signal interpretation field. Deep learning has been an important method for HRRP target recognition. However, recent research has shown that optical image target recognition methods based on deep learning are vulnerable to adversarial samples. Whether HRRP target recognition methods based on deep learning can be attacked remains an open question. In this paper, four methods of generating adversarial perturbations are proposed. Algorithm 1 generates the nontargeted fine-grained perturbation based on the binary search method. Algorithm 2 generates the targeted fine-grained perturbation based on the multiple-iteration method. Algorithm 3 generates the nontargeted universal adversarial perturbation (UAP) based on aggregating some fine-grained perturbations. Algorithm 4 generates the targeted universal perturbation based on scaling one fine-grained perturbation. These perturbations are used to generate adversarial samples to attack HRRP target recognition methods based on deep learning under white-box and black-box attacks. The experiments are conducted with actual radar data and show that the HRRP adversarial samples have certain aggressiveness. Therefore, HRRP target recognition methods based on deep learning have potential security risks.

HRRP Target Recognition With Deep Transfer Learning

Recently, radar high-resolution range profile (HRRP) recognition based on convolutional neural networks (CNNs) has received considerable attention due to its robustness to translation and amplitude changes. Most of the existing methods require that sufficient labeled data with complete aspect angles be used as training data, which is a difficult task in practice. In addition, HRRP signals have a high sensitivity to the aspect angle. Therefore, the representative and discriminative powers of the features extracted from typical CNN models are reduced due to incomplete aspect angles in the training data, which significantly limit the recognition performance. This paper first considers the problem of HRRP recognition with incomplete aspect angle training data and addresses the problem by a deep transfer learning framework. Specifically, the two proposed methods enhance the recognition performance by exploring the discriminative power and the intraclass consistency with auxiliary data, which have HRRP signals with complete aspect angles. This paper generates a simulated HRRP dataset from public data to validate the proposed work. The comparisons of the recognition results demonstrate that the proposed framework outperforms the latest CNN-based models.

Radar HRRP target recognition via statistics‐based scattering centre set registration

Due to the presence of noise and range resolution variation, each attribute of the scattering centre (SC) extracted from high-resolution range profile (HRRP) data is a random variable fluctuating around the real attribute. For the scatterer-based target recognition method, the extraction error will disturb the similarity measurement between SC sets and adversely affect the recognition performance. In this study, a novel approach for HRRP target recognition is proposed via the statistics-based SC set registration. The attribute set of SCs is first characterised by a Gaussian mixture model (GMM) in which each Gaussian component represents the corresponding SC attributes containing the amplitude and position. Besides, the noise power and range resolution are taken into account into the GMM parameters, which enhances the recognition robustness against noise and resolution variation. Afterwards, based on the different stability of SC attributes against the variation of target-sensor orientation, the weighted Gaussian quadratic form distance is developed to register the test Gaussian mixture with the templates. The resulting recognition method considers the statistical property of the estimated attribute of the SC and exhibits robust recognition performance. Experiments conducted on the measured data demonstrate the validity and robustness of the proposed method.