The success of deep learning in synthetic aperture radar (SAR) automatic target recognition (ATR) relies on a large number of labeled samples; however, there are often wrong (noisy) labels in a large-scale dataset. In this article, we propose a loss curve-fitting-based method, which can identify the noisy labels and train the classification network effectively. We propose to model label noise by unsupervised clustering via fitting loss curve to identify whether the sample’s label is clean or noisy. Then, we train the network using augmented samples with clean labels to correct noisy labels further. The experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset prove that our proposed method can deal with the situation when training a network with different ratios of noisy labels and correct noisy labels effectively. When the noise ratio is small (40%) in the training dataset, our method can correct 97.9% of noisy labels and train the classification network with 98.8% classification accuracy. While the noise ratio is large (80%), our method can correct 78.1% of noisy labels and train the classification network with 79.6% classification accuracy.

This article extends the shadow tracking for video-synthetic aperture radar (SAR) from a single-target framework to a multitarget framework, which is crucial for SAR ground moving targets’ identification. Inspired by FairMOT, the multitarget tracking framework for SAR shadow tracking is improved by using the triplet attention (TriAtt) mechanism and the lightweight multiscale network. By employing the ability to fuse spatial and feature dimensions of TriAtt and combining the lightweight network optimized by multiscale encoder–decoder and dilated convolution, a fast multiscale feature extraction module (FMsFEM) embedded with TriAtt is proposed for better tracking efficiency and performance. Experiments on the Sandiego video-SAR dataset validate that the TriAtt mechanism can improve the tracking performance of deep layer aggregation (DLA)-34, DLA-18, and FMsFEM significantly. FMsFEM with embedded TriAtt outperforms the state-of-the-art network (FairMOT with backbones of DLA-34 and DLA-18) with much faster frame rates. The average frame rates of FMsFEM and FMsFEM-TriAtt reach 60.32 and 56.13 fps for datasets with an image size of$1088\times 608$, which are about three times higher than the frame rates of others.

This article proposes a novel framework by combining a modified real-time recurrent regression (mRe³) network and a newly designed trajectory smoothing long short-term memory (LSTM) network for refocusing the ground moving target (GMT) in the synthetic aperture radar (SAR) image. The mRe^3 network that consists of a convolutional neural network (CNN) backbone and two LSTM modules is designed to track the GMT's shadow in an SAR video. Furthermore, we find that the complex trajectory obtained by the tracking network cannot directly be used for refocusing the GMT because of the estimation error. To address the abovementioned problem, a β-order total variation loss-based smoothing LSTM (TVβ-LSTM) is proposed to recover the GMT's trajectory to meet the requirement of refocusing. Besides, the effect of TVβ on the performance of smoothing LSTM is analyzed. By the experiments on simulated and real SAR videos, we find that the mRe^3 has stronger robustness and a better trajectory reconstruction precision compared with the existing tracking methods, especially for the strong interference cases. In addition, the smoothing LSTM can recover the trajectory of the GMT with higher precision and better smoothness. When β is set to 3, with the TVβ-LSTM, the center distance error of a recovered complex trajectory can be reduced from 0.82 to 0.782, while its fluctuation can be suppressed from 6 to 1 mm. By using our framework, the focused GMT with bountiful geometrical features can be obtained even for the K_a-band SAR.

Convolutional neural network (CNN) has become the mainstream method in the field of image recognition for its excellent ability to feature extraction. Most of the CNNs increase the classification accuracy for the rotational objects by imposing the network with rotation invariance or equivariance property, which causes the loss of the target's orientation information. In this work, a rotation-mapping network (RM-Net) that can achieve objects recognition and angle or orientation estimation simultaneously without additional network training is constructed. Besides, an octagona convolutional kernel is introduced to improve the network's performance. The experiments on the simulation SAR datasets show that the proposed RM-CNN can achieve state-of-the-art results in target recognition and angle estimation.

Deep neural networks have been widely applied and researched in synthetic aperture radar (SAR) object detection and achieved a great success. However, deep supervised networks heavily rely on a large amount of labeled data, while the annotation is difficult and time-consuming to obtain. But the unlabeled data are comparably easier to get. Considering that, we introduce a semi-supervised learning framework for SAR object detection, which is built via label propagation and consistent augmentation. The experiments on a SAR ship dataset prove that the introduced semi-supervised training framework can achieve higher detection performance with utilizing the unlabeled data compared with the corresponding supervised object detection network.

In synthetic aperture radar (SAR) automatic target recognition, it is expensive and time-consuming to annotate the targets. Thus, training a network with a few labeled data and plenty of unlabeled data attracts attention of many researchers. In this article, we design a semisupervised learning framework including self-consistent augmentation rule, mixup-based mixture, and weighted loss, which allows a classification network to utilize unlabeled data during training and ultimately alleviates the demand of labeled data. The proposed self-consistent augmentation rule forces the samples before and after augmentation to share the same labels to utilize the unlabeled data, which can ensure the prominent effect of supervised learning part of the framework for training by balancing amounts of labeled and unlabeled samples in a minibatch, and makes the network achieve better performance. Then, a mixture method is introduced to mix the labeled, unlabeled, and augmented samples for the better involvement of label information in the mixed samples. By using cross-entropy loss for the mixed-labeled mixtures and mean-squared error loss for the mixed-unlabeled mixtures, the total loss is defined as the weighted sum of them. The experiments on the MSTAR data set and OpenSARShip data set show that the performance of the method is not only far better than the state of the art among current semisupervised-based classifiers but also near to the state of the art among the supervised learning-based networks.

Deep neural networks have been widely applied and researched in remote sensing image scene classification and achieved a great success. However, deep supervised network heavily relies on a large amount of labeled data. The annotation is difficult and time-consuming to obtain but the unlabeled data are comparably easier to get. Considering that, we introduce a semi-supervised learning framework for remote sensing image scene classification. The network is trained by a novel adaptive perturbation training method. The experiments on NWPU-RESISC45 dataset prove that the introduced semi-supervised classification method can achieve higher classification accuracy with unlabeled data compared with the corresponding supervised classifier, and the designed adaptive perturbation training can further improve the performance of the semi-supervised learning-based classification network.

Conventional synthetic aperture radar (SAR) reconstructs the illuminated scene via fast Fourier transform (FFT), which results in high sidelobe level, and poor cross-range resolution due to the finite synthetic aperture length. In this paper, we formulate a sparse reconstruction method for SAR imaging based on the covariance fitting criterion, which assumes that only a few strong scatters exist in the whole scene. The method is able to fully control over the sparsity level and reconstruct the scenario in an adaptive manner. Experimental results with real SAR data show the better performance of our method compared with the conventional methods in terms of resolution improvement and sidelobe suppression.

In this paper, we propose to apply a deep multi-scale encoder-decoder neural network (MsEN-Net) for SAR images enhancement method based on scale-recurrent network (SRN), which consists of encoder and decoder modules trained by the coarse to fine strategy. Simulation with fixed speed errors and experiments with real data are implemented and evaluated by peak signal to noise ratio and structural similarity. Experimental results with both visual and quantitative analysis demonstrate the competitive performance of our proposed method.

Due to finite of information dimension of single bistatic synthetic aperture radar (SAR), to expand more information about ground objects we research multistatic SAR which can be decomposed into groups of bistatic SAR. It is known that scattering properties of the different viewing angles is different. In this paper, we focus on system of single transmitter and triple receivers and use polar format algorithm(PFA) to obtain images of ground objects for the triple receivers. A geometric distortion correction method is proposed due to elevation of ground objects. After the distortion correction, the three SAR images are registrated, then we put images into red, green, blue(RGB) channels respectively to realize fake color synthesis, and thus realize information fusion.

Coordinated multiple point transmission and reception can reduce system interference and Distributed Antenna Systems can enlarge wireless coverage with lower transmitting power. These two techniques introduce the flexibility of wireless network architecture design for green communications. In this paper, we extend wireless network architecture with cooperative distributed antenna systems and derive the downlink ergodic capacity in three different kinds of wireless coverage areas. Then, analytical expressions of the downlink ergodic capacity are verified by system simulation. The research results prove that combination of cooperative and DAS can provide beneficial to green wireless communications systems.

Base station joint transmission techniques based on QR decomposition(QRD) and Tomlinson-Harashima precoding (THP) can improve the cell-edge user BER performance. But the large spreading of the subchannel gains may degrade the performance severely. In this paper, an equal rate power allocation (ERPA) strategy and a modified zero-forcing(ZF) THP (M-ZF-THP) technique are introduced which can equalize the subchannel quality and improve the performance of the downlink coordinated multi-point transmission and reception (CoMP). Both techniques are based on the theorem that optimal bit-error-rate performance of the multi-antenna system can be achieved when spatial pipes have the same quality. “Best-first” ordering algorithm is used in M-ZF-THP to enhance the performance. Simulation results show that our proposed schemes are superior to QRD and ZF-THP based algorithms.