For the automatic target recognition (ATR) based on synthetic aperture radar (SAR) images, enough training data are required to effectively characterize target features and obtain good recognition performance. However, in practical applications, it is difficult to collect sufficient training data. To tackle the limitation, a novel end-to-end expansion method, called conditional Wasserstein deep convolutional generative adversarial network with gradient penalty (CWDCGAN), is proposed to achieve SAR image expansion with specified category. To be specific, the CWDCGAN innovatively designed a generative adversarial network architecture based on convolutional and deconvolution networks to improve the quality of generated images. At the same time, conditional information is introduced to control the categories of generated images, and Wasserstein distance and gradient penalty are used to modify the loss function, which makes the network training more stable. Besides, feature extraction and classifier design in a typical ATR system often rely heavily on subjective expert knowledge, which seriously affects its generalization performance. Therefore, a joint recognition method of Resnet18 and support vector machine (Renset18-SVM) is adopted to improve the generalization capacity and the recognition performance. Experimental results with public measured data show that the CWDCGAN can generate higher quality SAR images, and by feeding expanded data to Renset18-SVM, the recognition accuracy is improved under different proportions of training samples.

Compressed sensing (CS) based imaging technology has attracted a lot of interest because it can enhance imaging resolution. Targets of interest for forward-looking imaging radar are typically few in comparison to the entire imaging region. This sparsity allows for the natural application of CS to the reconstruction of high-resolution forward-looking images. However, conventional CS-based imaging methods can only perform well when the signal-to-noise ratio (SNR) is high. Strong noise in radar imaging prevents CS-based methods from producing excellent imaging results. Inspired by the low-rank propertity of the received radar target echo and the sparsity of the forward-looking image targets, we present a combined low-rank and sparse prior restricted model for forward-looking imaging with a multi-channel array radar to overcome strong noise. In order to solve the low-rank joint sparse double prior constraint optimization problem, an augmented Lagrange multiplier (ALM) reconstruction method under the framework of the alternating direction multiplier method (ADMM) is proposed. Finally, the results of simulation and real measurement data indicate that our presented method is fairly effective at enhancing the azimuth resolution and robustness of forward-looking radar imaging in comparison to other current methods.

Most synthetic aperture radar (SAR) automatic target recognition (ATR) methods can achieve good recognition results only under the closed-set assumption. However, in practical applications, ATR models are often exposed to open environments, the general closed-set method may misclassify unknown categories as known categories, which is not reasonable. To tackle this issue, this paper proposes an end-to-end hybrid model for SAR image open-set recognition (OSR), named GANFlow, which combines a generative adversarial network (GAN) with a flowbased module. The GANFlow achieves accurate classification of known categories and effective rejection of unknown categories. In this model, a classifiable convolution GAN is first designed to complete the training of the feature extraction module and classifier. Through adversarial training, the generated images enrich the training samples, which improves the ability of feature extraction and classification of the discriminator. Then, to find the difference in the probability density distribution of the extracted features, a flow-based module is adopted. Also, the features avoid interference from irrelevant background information in SAR images. Furthermore, by establishing an appropriate threshold, unknown categories can be efficiently rejected. Finally, the outputs of the classifier and the flow-based module are combined to complete the OSR of the SAR image target. The experimental results on the MSTAR and OpenSARShip publicmeasured datasets verify the robustness and generalization of the proposed method

General synthetic aperture radar (SAR) image automatic target recognition (ATR) methods perform well under standard operation conditions (SOCs). However, they are not effective in extended operation conditions (EOCs). To improve the robustness of the ATR system under various EOCs, an ATR method for SAR images based on the scattering parameter Gaussian mixture model (GMM) is proposed in this paper. First, an improved active contour model (ACM) is used for target–background segmentation, which is more robust against noise than the constant false alarm rate (CFAR) method. Then, as the extracted attributed scattering center (ASC) is sensitive to noise and resolution, the GMM is constructed using the extracted ASC set. Next, the weighted Gaussian quadratic form distance (WGQFD) is adopted to measure the similarity of GMMs for the recognition task, thereby avoiding false alarms and missed alarms caused by the varying number of scattering centers. Moreover, adaptive aspect–frame division is employed to reduce the number of templates and improve recognition efficiency. Finally, based on the public measured MSTAR dataset, different EOCs are constructed under noise, resolution change, model change, depression angle change, and occlusion of different proportions. The experimental results under different EOCs demonstrate that the proposed method exhibits excellent robustness while maintaining low computation time.

Multiple-input multiple-output (MIMO) radar uses waveform diversity technology to form a virtual aperture to improve the azimuth resolution of forward-looking imaging. However, the super-resolution imaging capability of MIMO radar is limited, and the resolution can only be doubled compared with the real aperture. In the radar forward-looking image, compared with the whole imaging scene, the target only occupies a small part. This sparsity of the target distribution provides the feasibility of applying the compressed sensing (CS) method to MIMO radar to further improve the forward-looking imaging resolution. At the same time, the forward-looking imaging method for a MIMO radar based on CS has the ability to perform single snapshot imaging, which avoids the problem of a motion supplement. However, the strong noise in the radar echo poses a challenge to the imaging method based on CS. Inspired by the low-rank properties of the received radar echoes and the generated images, and considering the existing information about sparse target distribution, a forward-looking super-resolution imaging model of a MIMO radar that combines sparse and double low-rank constraints is established to overcome strong noise and achieve robust forward-looking super-resolution imaging. In order to solve the multiple optimization problem, a forward-looking image reconstruction method based on the augmented Lagrangian multiplier (ALM) is proposed within the framework of the alternating direction multiplier method (ADMM). Finally, the results of the simulation and the measurement data show that the proposed method is quite effective at improving the azimuth resolution and robustness of forward-looking radar imaging compared with other existing methods.

In forward-looking imaging radar sensors, the targets of our focus are typically sparse in comparison to the whole imaging scene, which provides the feasibility of applying compressed sensing (CS) to forward-looking super-resolution imaging. However, sparsity is not satisfied in radar imaging when there is strong noise. This will result in the emergence of false targets and affect imaging results when traditional CS-based approaches are employed directly. In this paper, we propose a noise robust imaging method that combines echo denoising and noise weighting to solve this problem. Firstly, the denoised radar echo is generated using a complex variational mode decomposition (CVMD) method. The CVMD method is an extension of variational mode decomposition (VMD), which has been used to the traditional real signal denoising. According to the signal spectrum characteristics, we successfully applied the proposed CVMD method to radar echo denoising. Secondly, a noise weighting method based on l ₁ -norm is performed to further overcome strong noise. The weight comes from the CVMD denoising process, and it can better distinguish targets from the support area of noise. Lastly, simulated and measured data are utilized to verify the proposed method’s performance.