Radar transmits active agile waveformplays a significant role in anti-jamming. However, efficient coherent integration of target's agile echo in a coherent processing interval (CPI) usually poses a severe challenge. This letter proposes an improved sparsity adaptive matching pursue (ISAMP) to address this issue. Firstly, the agile echo signal model of random interpulse frequency and PRT joint agile (RI-FPrtJA) waveform is derived; The sparse reconstruction model of RI-FPrtJA echo signal is then mathematically deduced based on compress sensing theory; Lastly, the ISAMP is proposed to accurately accomplish sparse reconstruction based on the regularized grids mismatch correction and adaptive searching step extension. The simulation results demonstrate that the ISAMP method can achieve better coherent integration in terms of mismatch sidelode levels suppression, sparse reconstruction capacity, and computational cost, compared to some current existing methods.

Detecting ships in Synthetic Aperture Radar (SAR) images is a challenging task due to various factors, such as the diverse distribution of ships and the intricate nature of SAR images. In recent years, deep learning has made excellent progress in the field of SAR interpretation. Models that focus on extracting global semantic information can effectively achieve balanced detection of multi-scale SAR targets, but their computational complexity is relatively high. Models that focus on processing local information have redundant calculations and poor robustness, but are prone to mistaking the background information of SAR images for targets. To address the above issues, we propose a real-time SAR ship detector based on global semantic information enhancement and local gradient information mining. The lightweight feature extraction backbone based on linear computing is designed, with the network structure of Global Information Augmentation Encoder (GIAE)—Local Gradient Information Miner (LGIM)—Decoder, which can quickly perform feature extraction. GIAE enhances the expression of image content through the long sequence modeling capability of the State Space Model. LGIM uses gradient modules composed of depthwise separable convolutions to extract local information of image, and utilizes directed self-attention (DSA) to mine channel context information. GLDet can complete object detection, rotated object detection and instance segmentation tasks by transforming the detection head. Excellent performance has been achieved on the SAR ship instance segmentation dataset SSDD and HRSID, as well as the SAR rotated ship dataset RSDD-SAR and SSDD+. Meanwhile, GLDet demonstrated excellent generalization performance in large-scale SAR images captured by GF-3 and Terra-SAR satellites.

This article proposes a novel eigenvalue-based detector, called Lambda-1 detector, for adaptive and robust interference detection in single-look-complex (SLC) synthetic aperture radar (SAR) images. The proposed method leverages the increased eigenvalues caused by interference in SAR image blocks, where the interference is expected to have a small set of eigenvalues, particularly with a dominating one. Specifically, the method segments the image into multiple blocks, computes the eigenvalues of each block’s covariance matrix, and compares the largest eigenvalue$\lambda _{1}$with a threshold to determine the presence of interference under the criteria of constant false alarm rate (CFAR), thereby enabling adaptive interference detection against varying levels of interference-to-signal ratios (ISRs). The largest eigenvalue is characterized by the order-2 Tracy-Widom distribution (no closed-form expression) under the assumption of the image’s homogeneity, and the threshold is adaptively determined based on a scaled and shifted Gamma distribution that fits this distribution with a closed-form expression. The method is robust by first modeling and then correcting the impacts of upsampling and windowing of SAR image data on the fit distribution’s parameters, and by incorporating outlier removal preprocessing. Experimental results validate the effectiveness of the proposed method in successfully detecting both strong and weak interferences in various SAR images, including Sentinel-1 and Gaofen-3. The detection performance is quantitatively evaluated using false alarm rate$P_{\mathrm { fa}}$and detection rate$P_{d}$. In summary, the proposed Lambda-1 detector effectively identifies interference artifacts in focused SAR images and holds promise for improving the quality of SAR imagery by incorporating adaptive interference removal.

The active jamming suppression of Synthetic Aperture Radar (SAR) images remains a severe challenge. The jamming SAR images simulation dataset is firstly built by open SSDD dataset and random shift-frequency jamming type; The formula of existing diffusion model is then modified using the low-rank based block space filter (BSF) theory; Lastly, jamming SAR images are as the inputs to effectively train our proposed model to generate the jamming suppressed images. Experimental results qualitatively and quantitatively demonstrate the effectiveness of the proposed method.

Due to spectrum sharing spaceborne synthetic aperture radar (SAR) often experiences signal interference emitted by ground radio systems. Interference removal methods for SAR images are important measures to address this problem. Among these methods, block subspace filtering (BSF) has the advantage of removing various types of interference signals directly in single look complex (SLC) images. However, it assumes that the observation scene does not contain strong point scatterers, otherwise, BSF will have severe performance decline in terms of losing strong point scatterer intensity and causing horizontal or vertical black lines. This paper proposes a Robust version of BSF (RBSF), which can successfully overcome the above performance decline, thereby significantly improving the robustness of the algorithm. Specifically, RBSF uses a constant false alarm rate detector to detect and mask out strong scattering pixels from the SLC image. Then, BSF reconstructs the interference components from the SLC image with strong pixels being masked out, and finally subtracts them from the original SLC image. Moreover, we find that interference will reduce, to some extent, the image contrast and entropy. Based on this finding, we design an adaptive RBSF method which selects the subspace dimension parameter adaptively by means of optimizing the image contrast and entropy. Extensive experiments demonstrate that the RBSF algorithm achieves significant performance improvement over the original BSF algorithm.

The radon Fourier transform (RFT)-based coherent integration is an important target detection method. However, the computational cost of parameter searching and blind-speed sidelobe (BSSL) remain the main challenges in actual RFT applications. In this letter, we propose a Whale optimization algorithm-based RFT (WOA-RFT) to accelerate the parameter searching process and improve BSSL suppression performance. First, the discrete RFT signal model is derived; WOA-RFT is then proposed to speed up the RFT and suppress BSSL. The simulation results demonstrate that our proposed method has a better performance in terms of parameter estimation accuracy, BSSL suppression capability, and computational cost, compared to some existing methods.

The dense, complex and variable electromagnetic environment poses a serious challenge to radar signal sorting (RSS) in modern electronic reconnaissance systems. In order to improve RSS performance, this letter proposes a semi-supervised learning framework-based RSS method via a residual graph convolutional network (ResGCN-RSS) to effectively improve the generalization ability of the signal sorting models in small data scenarios. Firstly, the graph structure construction of intercepted radar signals is performed via K-nearest neighbor algorithm. Then, the three-layer ResGCN is designed to adaptively improve the features learning. Finally, RSS can be effectively and efficiently implemented through an end-to-end ResGCN with small labeled graph data of interleaved radar signals. The simulation experimental results show that our proposed method can achieve better average accuracy with little computational cost increasing when the labeled data is very small, compared to some existing methods.

In article[1], after drawing the curve of Fig. 4, we mistakenly marked two coordinate values in the fourth column of the figure to be the same as those in the third column. We modified the incorrectly marked coordinates 0.9585 and 0.9572 to the correct values 0.9489 and 0.9483, respectively. The error here is only a marking error; there is no change in the curve of the figure, and it does not affect the discussion and conclusion of this article or any other places. Since the article is early accessed in IEEE, we hope to correct it in the upcoming issue, thank you. The revised figure is shown below:

Speckle noise is an important factor affecting the accuracy of synthetic aperture radar (SAR) target recognition. Traditional speckle reduction methods based on transform domain and spatial filtering usually require professional experience to set the threshold, which will also affect the recognition accuracy. This letter proposes a multilevel wavelet speckle reduction network (Wavelet-SRNet) for noisy SAR images target recognition. First, the method designs the wavelet soft threshold denoising method as a trainable neural network module in the convolutional neural network (CNN) framework. Then, a two-level wavelet denoising branch is constructed and fused with the original noisy image. Finally, we cascade a CNN-based classification model on the above structure to form an SAR image target recognition network whose denoising threshold can be automatically learned. Experiments on the moving and stationary target acquisition and recognition database show that the classification accuracy of the proposed method for target recognition in noisy SAR images is better than the compared state-of-the-art methods. Also, the method achieved high test accuracy in the noise augmentation experiment.

Radar signal sorting (RSS) plays an important role in the electronic support measurement system. However, the existing clustering-based RSS methods depend heavily on prior knowledge to achieve excellent performance, which may bring severe challenges to RSS in actual scenarios. This letter proposes a novel subspace decomposition based adaptive density peak clustering (SD-ADPC) method to address the problems of low accuracy and high computational cost in RSS. First, the original complex radar signal data is directly decomposed into two-dimensional (2D) subspace by t-distributed random neighborhood embedding (t-SNE). Then, based on the outlier detection of the products between the peak density and the distance of the data points, ADPC is used to adaptively determine the optimal clustering centers of the original data in 2D subspace. Finally, the reminding data is assigned to its nearest cluster with Euclidean distance in one step. The experimental results of the simulated RSS dataset and the open baselines show that our proposed method does not require any knowledge and can achieve better or competitive performance in terms of accuracy and computational cost, compared to existing state-of-the-art methods.

Multiaccess edge computing (MEC) is a new paradigm to meet the requirements for low latency and high reliability of applications in vehicular networking. More computation-intensive and delay-sensitive applications can be realized through computation offloading of vehicles in vehicular MEC networks. However, the resources of a MEC server are not unlimited. Vehicles need to determine their task offloading strategies in real time under a dynamic-network environment to achieve optimal performance. In this article, we propose a multiuser noncooperative computation offloading game to adjust the offloading probability of each vehicle in vehicular MEC networks and design the payoff function considering the distance between the vehicle and MEC access point, application and communication model, and multivehicle competition for MEC resources. Moreover, we construct a distributed best response algorithm based on the computation offloading game model to maximize the utility of each vehicle and demonstrate that the strategy in this algorithm can converge to a unique and stable equilibrium under certain conditions. Furthermore, we conduct a series of experiments and comparisons with other offloading methods to analyze the effectiveness and performance of the proposed algorithms. The fast convergence and the improved performance of this algorithm are verified by numerical results.