Due to the target’s radar cross Section (RCS) changes with viewing angle and it is difficult to obtain target’s synthetic aperture radar (SAR) images of all the viewing angles, the training samples of SAR target recognition are always incomplete in the dimension of viewing angle. Aiming to solve the problem of views lacking in SAR image training samples, this letter proposes a multiview feature center (MVFC) sample expansion method. It is based on finding the angle-sensitive combination feature center of adjacent views. Through extracting combination features, it changes the image samples into feature dimension. Based on the correlation analysis, each sample’s similar samples could be found, and their equivalent centers are used as new sample features. By adding these centers, the sample amount could be doubled in feature dimension. At last, classifier could be trained by using the novel training sample to get better performance. This method transformed the image sample expansion problem into the feature sample expansion and used the multiview equivalent center to double the effective training sample. Experiments based on the moving and stationary target acquisition and recognition (MSTAR) dataset showed that the proposed method has higher recognition accuracy and robustness.

Although numerous classification algorithms based on deep learning can effectively extract valuable features from synthetic aperture radar (SAR) images to improve the accuracy of SAR automatic target recognition, most of them have rigorous constraints in reality and fail to obtain satisfying results with limited training samples. To address the above issues, we propose a novel multi-view attention capsule network for SAR target small sample recognition. In our method, after gaining the primary capsules of SAR images under different views, a capsule-based view attention module is designed to enhance the feature relations between different views. Then a joint dynamic routing mechanism is adopted to further capture the robust inter- and intra-image spatial relations to generate SAR capsules specialized in the final classification. Especially, to alleviate the impact of SAR target angle sensitivity on recognition, rotated cropping is applied to the original SAR images in advance. Finally, experimental results on the moving and stationary target recognition (MSTAR) and OpenSARShip dataset have demonstrated the superiority and robustness of the proposed method upon limited labeled samples.

The orthogonal waveform diversity design and efficient hardware implementation are important issues in miniature multiple-input multiple-output (MIMO) radars. The orthogonal frequency division multiplexing (OFDM) chirp waveform has received attention recently because of its large time-bandwidth product, constant modulus, no range-Doppler coupling, good orthogonality, and good Doppler tolerance. The dechirp-on-receive technique can reduce the amount of raw sampled data in near-field miniature millimeter-wave (mmW) MIMO radar detection and synthetic aperture radar (SAR) imaging. However, most of the current waveform diversity design methods are based on general matched filtering (MF). In this paper, the possibility of using the traditional OFDM chirp waveform for dechirp processing at the receiving end of MIMO radar is analyzed. Then, the results of different configurations of chirp rates within and between transmitted waveforms for different signal processing procedures are investigated. A novel dechirp-based OFDM chirp waveform diversity design method for MIMO radar is proposed, and the results of the waveform design are given. Numerical results, such as pulse compression (PC) results, dechirp ambiguity function (DAF), SAR imaging processing, etc., and experiments verify the effectiveness of the proposed methods.

Deep learning models have achieved remarkable performance in synthetic aperture radar (SAR) target recognition. However, the accuracy of these methods is sensitive to the hyper-parameters and the traditional backpropagation is time consuming. In this letter, we proposed a modified convolutional random vector functional link (IntCRVFL) network for SAR target recognition, which can simplify the SAR target recognition system. The CRVFL network consists of a convolutional neural network and an RVFL network. First, the fixed convolutional layers with randomly initialized parameters extract SAR image features and then the RVFL network performs target recognition. Especially, inspired by hyperdimensional computing, the activations of the hidden layer are obtained through a new encoding manner. Besides, only the connections between hidden and output layers need to train by a closed-form solution for the ultimately precise target recognition. The experimental results on the moving and stationary target acquisition and recognition (MSTAR) dataset demonstrate the proposed IntCRVFL network can obtain a satisfying accuracy with a faster speed.

Deep-learning-based methods have obtained satisfying results in polarimetric synthetic aperture radar (PolSAR) image classification. However, these methods require large numbers of labeled samples, which are usually time-consuming and high-priced for PolSAR images. To address this issue, a semisupervised method based on a 3-D convolutional neural network (3-D-CNN) using pseudo labels (PL-3-D-CNN) is proposed. First, the coherency matrix of PolSAR data is converted into a 6-D real-valued vector by a unitary transformation. Then, the K-means algorithm is utilized for generating pseudo labels. After that, labeled samples and pseudo labeled samples are fed into the PL-3-D-CNN model to extract supervised and unsupervised features. Finally, the supervised and unsupervised features are combined to improve classification accuracy. The proposed method is tested on both AIRSAR and RADARSAT-2 data sets. The results show that the proposed method is an effective method for PolSAR image classification and shows good performance under a small number of labeled samples. The source code for the PL-3-D-CNN model is available athttps://github.com/fangzheng-nuaa/PL-3D-CNN.

Polarimetric synthetic aperture radar (PolSAR) image classification is an essential part of PolSAR image interpretation. In recent years, convolutional neural networks (CNNs) have made significant advances in PolSAR image classification. However, the current CNN-based methods ignore complementary information among different feature maps and correlations between elements of coherence matrix, which can provide discriminative information for classification. Besides, the phase information contained in the complex-valued (CV) coherence matrix cannot be extracted effectively. In this letter, a stacked CV convolutional long short-term memory (ConvLSTM) network called CV-ConvLSTM is proposed for PolSAR classification. Compared to existing methods, CV-ConvLSTM can extract complementary information among different feature maps and utilize the dependencies of elements in the coherency matrix, which can improve the performance of classification. In addition, the CV operations are added to the network, in which phase information is used for better classification. The experimental results of two widely used PolSAR datasets demonstrate that CV-ConvLSTM can obtain superior performance compared with existing CNN methods.

The orthogonal waveform design and good hardware realization of multiple-input–multiple-output (MIMO) radar have always been important research topics. The orthogonal frequency division multiplexing (OFDM) chirp waveform has received more attention because of its large time-bandwidth product, constant modulus, no range-Doppler coupling, good orthogonality, and good Doppler tolerance. Dechirp technique can reduce the amount of raw sampled data very well in near-field miniature lightweight MIMO radar detection and synthetic aperture radar (SAR) imaging. However, most of the current waveform analysis methods are based on matched filtering (MF). In this letter, an ambiguity function (AF) based on the dechirp signal processing approach to analyze the waveform performance is proposed, called dechirp ambiguity function (DAF). The pulse compression performance of the waveform itself and the level of mutual interference between the waveforms are described from the perspective of DAF. Numerical results validate reliability and effectiveness of the DAF.