Radar Target Recognition Method Research Articles

Split_ Composite: A Radar Target Recognition Method on FFT Convolution Acceleration.

Synthetic Aperture Radar (SAR) is renowned for its all-weather and all-time imaging capabilities, making it invaluable for ship target recognition. Despite the advancements in deep learning models, the efficiency of Convolutional Neural Networks (CNNs) in the frequency domain is often constrained by memory limitations and the stringent real-time requirements of embedded systems. To surmount these obstacles, we introduce the Split_ Composite method, an innovative convolution acceleration technique grounded in Fast Fourier Transform (FFT). This method employs input block decomposition and a composite zero-padding approach to streamline memory bandwidth and computational complexity via optimized frequency-domain convolution and image reconstruction. By capitalizing on FFT's inherent periodicity to augment frequency resolution, Split_ Composite facilitates weight sharing, curtailing both memory access and computational demands. Our experiments, conducted using the OpenSARShip-4 dataset, confirm that the Split_ Composite method upholds high recognition precision while markedly enhancing inference velocity, especially in the realm of large-scale data processing, thereby exhibiting exceptional scalability and efficiency. When juxtaposed with state-of-the-art convolution optimization technologies such as Winograd and TensorRT, Split_ Composite has demonstrated a significant lead in inference speed without compromising the precision of recognition.

SAR Target Recognition Method based on Adaptive Weighted Decision Fusion of Deep Features

Background: This paper proposes a synthetic aperture radar (SAR) target recognition method based on adaptive weighted decision fusion of multi-level deep features. Methods: The trained ResNet-18 is employed to extract multi-level deep features from SAR images. Afterwards, based on the joint sparse representation (JSR) model, the multi-level deep features are represented to obtain the corresponding reconstruction error vectors. Considering the differences in the abilities of different levels of features to distinguish the target, the reconstruction error vectors are analyzed based on entropy theory, and their corresponding weights are adaptively obtained. Finally, the fused reconstruction error result is obtained through adaptively weighted fusion, and the target label is determined accordingly. Results: Experiments are conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset under different conditions, and the proposed method is compared with published methods, including multi-feature decision fusion, JSR-based decision fusion and other types of ResNets. Conclusion: The experimental results under standard operating condition (SOC) and extended operating conditions (EOCs) including depression angle variance and noise corruption validate the advantages of the proposed method.

The Process Analysis Method of SAR Target Recognition in Pre-Trained CNN Models.

Recently, attention has been paid to the convolutional neural network (CNN) based synthetic aperture radar (SAR) target recognition method. Because of its advantages of automatic feature extraction and the preservation of translation invariance, the recognition accuracies are stronger than traditional methods. However, similar to other deep learning models, CNN is a "black-box" model, whose working process is vague. It is difficult to locate the decision reasons. Because of this, we focus on the process analysis of a pre-trained CNN model. The role of the processing to feature extraction and final recognition decision is discussed. The discussed components of CNN models are convolution, activation function, and full connection. Here, the convolution processing can be deemed as image filtering. The activation function provides a nonlinear element of processing. Moreover, the fully connected layers can also further extract features. In the experiment, four classical CNN models, i.e., AlexNet, VGG16, GoogLeNet, and ResNet-50, are trained by public MSTAR data, which can realize ten-category SAR target recognition. These pre-trained CNN models are processing objects of the proposed process analysis method. After the analysis, the content of the SAR image target features concerned by these pre-trained CNN models is further clarified. In summary, we provide a paradigm to process the analysis of pre-trained CNN models used for SAR target recognition in this paper. To some degree, the adaptability of these models to SAR images is verified.

Multiview synthetic aperture radar target recognition method using joint sparse representation and random weight matrix

Multiview synthetic aperture radar (SAR) images could provide richer information for target recognition as reported in previous works. This study exploits multiview SAR images with application to target recognition via the combination of joint sparse representation (JSR) and random weight matrix. First, the multiview SAR images are clustered into several view sets via a coarse clustering algorithm. In this way, the views in each set tend to share high correlations so they can be properly represented with JSR. For the reconstruction errors from different views, they are fused through a random weight matrix, which includes a rich set of linear weight vectors. Afterwards, the fused errors at different choices of weight vectors are analyzed to form a decision value for target recognition, which could better reflect the differences between different classes through multiview SAR images in comparison with traditional JSR-based methods. Therefore, the overall SAR target recognition performance can be effectively enhanced. Experiments are investigated on the moving and stationary target acquisition and recognition dataset under several operating conditions. The results reveal the high effectiveness of the proposed method under standard operating condition and good robustness under typical extended operating conditions including depression angle variance, configuration variants, noise corruption, partial occlusion, and resolution variance.

Highly Robust Synthetic Aperture Radar Target Recognition Method Based on Simulation Data Training

Sufficient synthetic aperture radar (SAR) data is the key element in achieving excellent target recognition performance for most deep learning algorithms. It is unrealistic to obtain sufficient SAR data from the actual measurements, so SAR simulation based on electromagnetic scattering modeling has become an effective way to obtain sufficient samples. Simulated and measured SAR images are nonhomologous data. Due to the fact that the target geometric model of SAR simulation is not inevitably consistent with the real object, the SAR sensor model in SAR simulation may be different from the actual sensor, the background environment of the object is also inevitably different from that of SAR simulation, the error of electromagnetic modeling method itself, and so on. There are inevitable differences between the simulated and measured SAR images, which will affect the recognition performance. To address this problem, an SAR simulation method based on a high-frequency asymptotic technique and a discrete ray tracing technique is proposed in this paper to obtain SAR simulation images of ground vehicle targets. Next, various convolutional neural networks (CNNs) and AugMix data augmentation methods are proposed to train only on simulated data, and then target recognition on MSTAR measured data is performed. The experiments show that all the CNNs can achieve incredible recognition performance on the nonhomologous SAR data, and the RegNetX-3.2GF achieves state-of-the-art performance, up to 84.81%.

Radar Target Recognition and Location Based on CapsNetv2

For precise detection and positioning of weapons and equipment under complex ground backgrounds and weather-changing aerial backgrounds. Compared with the traditional convolutional neural networks, the Capsule Network (CapsNet) is more suitable for identifying weapons and equipment in complex backgrounds because it uses vectors as input for the first time, which can well retain the characteristic information such as the direction and the angle of the target. Therefore, this paper proposes a radar target classification algorithm based on the combination of CapsNetv2 and infrared lidar, which simplifies the convolutional layer of the traditional 9 × 9 capsule network through a 1 × 1 reduction layer and a 3 × 3 convolution kernel, and adopts a double-layer capsule layer. Two prediction frames are obtained to improve the recognition accuracy; at the same time, the output volume retains the direction and the angle, which can more accurately classify the radar targets in various complex backgrounds. Applying the method proposed in this article to the MSTAR dataset shows that the radar target positioning is accurate. The rate increases to 99.5%. Finally, compared with the AlexNet and the YOLOv4 methods designed by Alex Krizhevsky, the proposed radar target recognition method can accurately and quickly identify weapons and equipment from complex backgrounds. The results obtained from the CapsNetv2 are accurately compared with other methods’ in complex backgrounds. The proposed method significantly improves the efficiency of military inspections.

A SAR Target Recognition Method Based on Decision Fusion of Multiple Features and Classifiers

A synthetic aperture radar (SAR) target recognition method combining multiple features and multiple classifiers is proposed. The Zernike moments, kernel principal component analysis (KPCA), and monographic signals are used to describe SAR image features. The three types of features describe SAR target geometric shape features, projection features, and image decomposition features. Their combined use can effectively enhance the description of the target. In the classification stage, the support vector machine (SVM), sparse representation-based classification (SRC), and joint sparse representation (JSR) are used as the classifiers for the three types of features, respectively, and the corresponding decision variables are obtained. For the decision variables of the three types of features, multiple sets of weight vectors are used for weighted fusion to determine the target label of the test sample. In the experiment, based on the MSTAR dataset, experiments are performed under standard operating condition (SOC) and extended operating conditions (EOCs). The experimental results verify the effectiveness, robustness, and adaptability of the proposed method.

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.

Radar target shape recognition using a gated recurrent unit based on RCS time series' statistical features by sliding window segmentation

The radar cross section (RCS) is an important parameter that reflects the scattering characteristics of radar targets. Based on the monostatic radar RCS time series' statistical features by sliding window segmentation, a novel sliding window-statistical-gated recurrent unit (SW-S-GRU) method for radar target recognition (RTR) is proposed using a GRU. The sliding window method divides the RCS time series into many segments for obtaining the statistical features as the input of the GRU. The deeper features are extracted from the segmented time series and then classified by the GRU. Under the model of precession motion, simulation results show that the proposed method achieves a great recognition accuracy. Even under the condition of a low SNR, the proposed method can obtain better recognition accuracy. For the precession dataset, the effect of sliding window parameters on RTR is analysed. The simulation results show that the recognition result of the model is better using the longer series overlap length between adjacent times. And when the number of hidden layers is 8 or above 8, the recognition accuracy does not change. Using the publicly available measured drone RCS data, the proposed method can also identify the drone well.

Synthetic aperture radar target recognition based on joint classification of selected monogenic components by nonlinear correlation information entropy

A synthetic aperture radar (SAR) target recognition method is proposed using monogenic components as basic features. The monogenic signal is employed to decompose original SAR images into multi-scale components. Considering the redundancy and possible indiscrimination in the monogenic components, the nonlinear correlation information entropy (NCIE) is adopted as the criteria for the selection of valid components. The subset of monogenic components with the highest NCIE is chosen and classified by joint sparse representation (JSR). Using the inner correlations of the selected components, JSR could improve the overall reconstruction precision thus enhancing the recognition performance. Experiments are proceeded on the moving and stationary target acquisition and recognition dataset under the standard operating condition and several extended operating conditions, including configuration variances, depression angle variances, noise corruption, and partial occlusion. The results validate the superior effectiveness and robustness of the proposed method over several existed SAR target recognition methods.

Target Recognition of SAR Images Based on SVM and KSRC.

A synthetic aperture radar (SAR) target recognition method combining linear and nonlinear feature extraction and classifiers is proposed. The principal component analysis (PCA) and kernel PCA (KPCA) are used to extract feature vectors of the original SAR image, respectively, which are classical and reliable feature extraction algorithms. In addition, KPCA can effectively make up for the weak linear description ability of PCA. Afterwards, support vector machine (SVM) and kernel sparse representation-based classification (KSRC) are used to classify the KPCA and PCA feature vectors, respectively. Similar to the idea of feature extraction, KSRC mainly introduces kernel functions to improve the processing and classification capabilities of nonlinear data. Through the combination of linear and nonlinear features and classifiers, the internal data structure of SAR images and the correspondence between test and training samples can be better investigated. In the experiment, the performance of the proposed method is tested based on the MSTAR dataset. The results show the effectiveness and robustness of the proposed method.

Region Matching of SAR Images Using Blocks for Target Recognition.

A synthetic aperture radar (SAR) target recognition method based on image blocking and matching is proposed. The test SAR image is first separated into four blocks, which are analyzed and matched separately. For each block, the monogenic signal is employed to describe its time-frequency distribution and local details with a feature vector. The sparse representation-based classification (SRC) is used to classify the four monogenic feature vectors and produce the reconstruction error vectors. Afterwards, a random weight matrix with a rich set of weight vectors is used to linearly fuse the feature vectors and all the results are analyzed in a statistical way. Finally, a decision value is designed based on the statistical analysis to determine the target label. The proposed method is tested on the moving and stationary target acquisition and recognition (MSTAR) dataset and the results confirm the validity of the proposed method.

Multivariate empirical mode decomposition with application to SAR image target recognition

A synthetic aperture radar (SAR) target recognition method was proposed based on multivariate empirical mode decomposition (MEMD). MEMD was the general extension of traditional EMD, which could avoid the mode mixing problems. MEMD was employed to process SAR images to obtain the multi-layer intrinsic mode functions (IMF), which could better reflect the time-frequency properties of the targets. Different layers of IMFs could effectively complement each other while sharing some inner correlations because they are generated from the same target. In the classification phase, the joint sparse representation was employed to represent the IMFs. The joint sparse representation could solve several related sparse representation tasks based on the idea of multi-task learning. It could produce more precise estimations than the solutions of single tasks. According to the sparse coefficient vectors corresponding to different IMFs, the reconstruction errors of different classes for the representation of the test sample can be calculated. Afterwards, the target label of the test sample can be determined. Experiments were conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, by comparison with existing methods under the standard operating condition, depression angle variance, noise corruption, and target occlusion, the results confirm the validity of the proposed method.

Combination of multiple decision principles based on sparse representation-based classification for target recognition of SAR image

A synthetic aperture radar (SAR) target recognition method using multiple decision principles in sparse representation-based classification (SRC) was proposed. The traditional SRC generally reconstructed the test sample on the global dictionary and calculated the reconstruction errors of individual training classes. And the decision was reached based on the minimum reconstruction error. However, because of the complexity of SAR target recognition, a single decision principle probably had low adaptivity to the extended operating conditions (EOC). Therefore, this paper employed the global minimum reconstruction, maximum coefficient energy, and local minimum reconstruction error principles to make decisions based on the solved coefficient vector from sparse representation. The global minimum reconstruction error principle directly adopted the traditional one. The maximum coefficient energy principle calculated the coefficient energies of different classes and made decision based on the maximum one. The local minimum reconstruction error principle represented and analyzed the test sample on the local dictionary so the azimuthal sensitivity of SAR imaging could be exploited. For the decision values from the three principles, they were transformed to the same type of probability vectors. Finally, the linear fusion was performed to combine their decisions. Experiments were conducted on the MSTAR dataset under situations including the standard operating condition (SOC), depression angle variance, noise corruption, and target occlusion. The results validate that the combination of multiple decision principles could effectively improve SAR target recognition performance.

Specific emitter identification of radar based on one dimensional convolution neural network

Specific Emitter Identification (SEI) of radar has become a focus point of research and difficulty in the field of electronic reconnaissance, and the application of Automatic Dependent Surveillance Broadcast (ADS-B) signal in Identification Friend or Foe (IFF) system is beginning to raise increasing concern. At present, the research of deep neural network in the field of target recognition mostly focuses on the modulation pattern recognition of signal, but there are few applications for the specific emitter identification. In order to overcome the shortcoming that the effect of the traditional method is not excellent, a radar emitter target recognition method based on one-dimensional(1D) convolutional neural network is proposed for ADS-B signal in this paper. Taking the sequence data of ADS-B pulse signals as the training and testing samples of emitter target identification, the frequency domain features of ADS-B signal are obtained by Fast Fourier Transform (FFT), and the target identification is achieved by combining the convolution neural network with the fine feature extraction. The result of simulation demonstrates that the specific emitter identification based on frequency domain and one-dimensional(1D) convolutional neural network has an excellent recognition performance, which effectively resolves the shortcomings of the traditional method of feature extraction and low classification accuracy.

Feature Extraction Method for DCP HRRP-Based Radar Target Recognition via $m-\chi$ Decomposition and Sparsity-Preserving Discriminant Correlation Analysis

Dual-circular polarimetric (DCP) high-resolution range profile (HRRP) provides similar target information to the full polarimetric HRRP and has the same data volume as the dual-linear polarimetric HRRP, thus it is significant to investigate the capability of DCP HRRP for target recognition. In this paper, a novel feature extraction method is proposed for the DCP HRRP-based target recognition. First, due to the good capability of characterizing the target polarimetric structure, the $m - \chi $ decomposition is exploited to obtain three scattering components of targets corresponding to the odd-bounce, even-bounce and randomly polarized scattering mechanisms along the radar line-of-sight (LOS), respectively. However, these three scattering components are infeasible to be directly utilized for target recognition due to the high dimensionality and redundancy. Considering this, a novel feature fusion method named as sparsity-preserving discriminant correlation analysis (SPDCA) is proposed to fuse the three scattering components. The SPDCA method can obtain the low-dimensional projection feature with good class separability by preserving both the global structure and local sparsity property of the original data. Besides, the redundancy of the three scattering components is eliminated by the SPDCA method. The results of experiments conducted on the simulated data of 10 civilian vehicles and real data of 3 military vehicles demonstrate the effectiveness and robustness of the proposed feature extraction method.

Adversarial attacks on deep-learning-based SAR image target recognition

Synthetic aperture radar (SAR) image target recognition has consistently been a research hotspot in the field of radar image interpretation. Compared with traditional target recognition algorithms, SAR target recognition algorithms based on deep learning offer end-to-end feature learning, which can effectively improve the target recognition rate, making them an important method for radar target recognition. However, recent research shows that optical image recognition methods based on deep learning are vulnerable to adversarial examples. In SAR image target recognition, whether adversarial examples exist for deep learning algorithms is still an open question. This paper uses three mainstream algorithms to generate adversarial examples to attack three classical deep learning algorithms for SAR image target recognition. The experiments involve publicly real SAR images for white-box and black-box attacks. The results show that SAR target recognition algorithms based on deep learning are potentially vulnerable to adversarial examples.

Target Recognition of Synthetic Aperture Radar Images Based on Two-Phase Sparse Representation

A synthetic aperture radar (SAR) target recognition method is proposed via linear representation over the global and local dictionaries. The collaborative representation is performed on the local dictionary, which comprises of training samples from a single class. Then, the reconstruction errors as for representing the test sample reflect the absolute representation capabilities of different training classes. Accordingly, the target label can be directly decided when one class achieves a notably lower reconstruction error than the others. Otherwise, several candidate classes with relatively low reconstruction errors are selected as the candidate classes to form the global dictionary, based on which the sparse representation-based classification (SRC) is performed. SRC also produces the reconstruction errors of the candidate classes, which reflect their relative representation capabilities for the test sample. As a comprehensive consideration, the reconstruction errors from the collaborative representation and SRC are fused for decision-making. Therefore, the proposed method could inherit the high efficiency of the collaborative representation. In addition, the selection of the candidate training classes also relieves the computational burden during SRC. By combining the absolute and relative representation capabilities, the final classification accuracy can also be improved. During the experimental evaluation, the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset is employed to test the proposed method under several different operating conditions. The proposed method is compared with some other SAR target recognition methods simultaneously. The results show the superior performance of the proposed method.

Target Recognition in SAR Images Based on Multiresolution Representations with 2D Canonical Correlation Analysis

This study proposes a synthetic aperture radar (SAR) target-recognition method based on the fused features from the multiresolution representations by 2D canonical correlation analysis (2DCCA). The multiresolution representations were demonstrated to be more discriminative than the solely original image. So, the joint classification of the multiresolution representations is beneficial to the enhancement of SAR target recognition performance. 2DCCA is capable of exploiting the inner correlations of the multiresolution representations while significantly reducing the redundancy. Therefore, the fused features can effectively convey the discrimination capability of the multiresolution representations while relieving the storage and computational burdens caused by the original high dimension. In the classification stage, the sparse representation-based classification (SRC) is employed to classify the fused features. SRC is an effective and robust classifier, which has been extensively validated in the previous works. The moving and stationary target acquisition and recognition (MSTAR) data set is employed to evaluate the proposed method. According to the experimental results, the proposed method could achieve a high recognition rate of 97.63% for the 10 classes of targets under the standard operating condition (SOC). Under the extended operating conditions (EOC) like configuration variance, depression angle variance, and the robustness of the proposed method are also quantitively validated. In comparison with some other SAR target recognition methods, the superiority of the proposed method can be effectively demonstrated.

Using Polarization-Diverse Wave-Coefficients for Aerospace Target Recognition

Wave-coefficients (WCs) based target recognition can be theoretically applied to any a frequency range, with increased sensitivity to aspect angle at higher frequencies. When it is utilized to a frequency range in resonance region, the WCs can tolerate fairly big aspect variation and contain enough target information, bringing a competitive advantage in multitudinous target features. Four polarization channel WCs are introduced and independently utilized to identify four aerospace targets under noise and noise-free circumstances. To take full advantage of the WCs in different polarization channels, a novel radar aerospace target recognition method based on multi-polarization channel WCs is proposed in this article. The majority vote rule and a maximum discrepancy rule are combined to identify a target when four polarization channel WCs are available at the same time. Simulation results on four aircraft models show that the proposed technique achieves a recognition performance at least comparable to the best single channel performance.