While unencrypted information inspection in physical layer (e.g., open headers) can provide deep insights for optimizing wireless networks, the state-of-the-art (SOTA) methods heavily depend on full sampling rate (a.k.a Nyquist rate), and high-cost radios, due to terrestrial and non-terrestrial networks densely occupying multiple bands across large bandwidth (e.g., from 4G/5G at 0.4–7 GHz to LEO satellite at 4–40 GHz). To this end, we present SigChord, an efficient physical layer inspection system built on low-cost and sub-Nyquist sampling radios. We first design a deep and rule-based interleaving algorithm based on Transformer network to perform spectrum sensing and signal recovery under sub-Nyquist sampling rate, and second, cascade protocol identifier and decoder based on Transformer neural networks to help physical layer packets analysis. We implement SigChord using software-defined radio platforms, and extensively evaluate it on over-the-air terrestrial and non-terrestrial wireless signals. The experiments demonstrate that SigChord delivers over 99% accuracy in detecting and decoding, while still decreasing 34% sampling rate, compared with the SOTA approaches.

Synthetic aperture radar (SAR) image change detection plays a crucial role in monitoring environmental changes and landform evolution. However, existing methods often struggle to retain details while reducing computational cost, or lack the ability to effectively incorporate global information, especially under complex and noisy conditions. To address these challenges, we propose a novel global robust structured graph approach with difference-aware filtering (GRSG-DAF), which effectively enhances the detection of changed areas by combining pixel-level information with the advantages of the superpixel-based structured graph. First, we construct a structured graph by integrating difference information, utilizing superpixel co-segmentation and adaptive affinity weighting, thus significantly reducing computational complexity and preserving critical structural patterns. Second, an image reconstruction process is implemented, utilizing a feature propagation mechanism to improve the contextual representation of the image. Finally, a difference-aware guided filtering process is developed by integrating the inherent guidance from the original pixel-level image, preserving boundary structures and spatial details. Experimental results demonstrate that our proposed method outperforms state-of-the-art methods on five benchmark datasets, achieving higher accuracy while balancing effectiveness and efficiency in change detection.

Due to sophisticated deployments of all kinds of wireless networks (e.g., 5G, Wi-Fi, Bluetooth, LEO satellite, etc.), multiband signals distribute in a large bandwidth (e.g., from 70 MHz to 8 GHz). Consequently, for network monitoring and spectrum sharing applications, a sniffer for extracting physical layer information, such as structure of packet, with low sampling rate (especially, sub-Nyquist sampling) can significantly improve their cost- and energy-efficiency. However, to achieve a multiband signals sniffer is really a challenge. To this end, we propose Sums, a system that can sniff and analyze multiband signals in a blind manner. Our Sums takes advantage of hardware and algorithm co-design, multi-coset sub-Nyquist sampling hardware, and a multi-task deep learning framework. The hardware component breaks the Nyquist rule to sample GHz bandwidth, but only pays for a 50 MSPS sampling rate. Our multi-task learning framework directly tackles the sampling data to perform spectrum sensing, physical layer protocol recognition, and demodulation for deep inspection from multiband signals. Extensive experiments demonstrate that Sums achieves higher accuracy than the state-of-the-art baselines in spectrum sensing, modulation classification, and demodulation. As a result, our Sums can help researchers and end-users to diagnose or troubleshoot their problems of wireless infrastructures deployments in practice.

Within the framework of Random Finite Set theory, a trajectory initiation method called Multi-dimensional Screening of Plot (MDSP) is proposed. This method realizes plot screening by constraining the discreteness, signal-to-noise ratio, motion state, and formation configuration of plots. Based on the screening results, different levels are assigned to the plot information, and then high-quality plots are selected to complete the tracking initiation. Simulation experiments verify that the proposed algorithm has better tracking performance than traditional algorithms in dense clutter, with less computational complexity and significantly accelerated response speed to newly born targets. The results show that the proposed method has strong robustness and can achieve accurate tracking of multiple targets under low signal-to-noise ratio.

Change detection (CD) methods for remote sensing images based on deep learning have garnered increasing research attention. However, existing deep learning approaches are often tailored for specific types of sensors. Extending these methods to dual-sensor scenarios presents challenges, including difficulties in data fusion and an increase in parameter numbers. To address these challenges, we propose a novel dual-stream encoder–decoder CD network architecture. In the encoder, the architecture comprises a shared-weight Siamese Unet stream for each sensor, with unique weights for different sensors. Before the decoder, a 3-D attention module (3-D AM) is incorporated, processing encoder outputs and fusing features from different streams. In addition, to mitigate the increased model parameter numbers due to the use of dual sensors, we propose a lightweight Unet architecture along with a time-difference structure in each stream. The proposed model is evaluated across multiple scenarios on a dual-sensor CD dataset, yielding an F1 score of 0.572 and the parameter number of 0.91 M. These results showcase high performance on a cost-effective level. Our code is available athttps://github.com/CodeofHuang/SmaDS_SiamUnet.

In recent years, researches on the recognition for Inverse Synthetic Aperture Radar (ISAR) images continue to deepen, while most methods only use the amplitude information of the ISAR image data. Besides, high-order terms in the complex-valued (CV) received signals for maneuvering space targets will cause defocusing on the ISAR images, which affects the accuracy of the recognition. For a steadily rotating maneuvering target, its high-order phase information between frames is relevant, and this information can be used to facilitate recognition. To this end, this letter proposes an end-to-end recognition framework in the CV domain based on the transformer model. It uses multi-scale feature extraction strategy and CV attention mechanism to get the local and global hybrid feature. Besides, A spatio-temporal transformer (STT) block is proposed to obtain the spatio-temporal correlation between image frames to assist recognition. Finally, a residual CNN block is introduced to promote diversity in the captured representations. In the experimental part, the recognition results of the proposed method on the real and simulated dataset are better than those of other methods. Compared with the classic sequence recognition method CVLSTM, the recognition accuracy and kappa coefficient of the proposed method are increased by approximately 5.6% and 5.4% respectively.

Aiming at the three-dimensional rotation speed estimation task of a synthetic aperture radar (SAR) ship target, this paper proposes a complex-valued convolutional long short-term memory (CV-ConvLSTM) network. It can simultaneously perceive the time, space and frequency domain information of the complex SAR imagery sequence. All elements of convolutional long short-term memory (ConvLSTM) network including convolutional layer, activation function, input gate, forget gate, and output gate are extended to the complex domain. In order to verify the superiority of CV-ConvLSTM in frequency domain information perception over ConvLSTM. Experiments show that CV-ConvLSTM has higher estimation accuracy than ConvLSTM.

Since synthetic aperture radar (SAR) can observe all-weather, it is widely used in ship target detection and segmentation tasks. However, SAR images have complex backgrounds and clutter interference, which affect the segmentation accuracy. This paper proposes a saliency-combined complex-valued U-Net. The network consists of two parts, namely complex-valued U-Net(CV-UNet) and original U-Net. The CV-UNet is used to process the measured data of SAR images which contains amplitude and phase information. The original U-Net is used to process the saliency map generated by the SAR image, and the results of two parts of the network output are connected. The experiment uses the measured data of HISEA-1 to make a target segmentation dataset, and uses the trained network for testing. The results show that the performance of the proposed method is better than that of the original U-Net and CV-UNet.

The automatic registration of multi-modal remote sensing data (such as optical and SAR) is a challenging task because of the significant non-linear radiation difference between these data, as well as the transformation of rotation and scaling. In order to solve the above problems, this paper proposes a two-step strategy. Firstly, the improved HOPC algorithm is introduced to obtain matching point pairs, and then a weighted strategy is used to calculate the global affine transformation matrix. In the first step, an improved Harris operator is used to extract the points of interest, and the matching accuracy is maintained while the amount of calculation is reduced. Then the matching loss calculated by the HOPC algorithm is used to calculate the contribution weight of the image block, which is utilized to calculate the global transformation matrix. The results show that the method in this paper has strong robustness to complex nonlinear radiation differences, and the two-step strategy greatly improves the accuracy of matching, which is better than similar matching algorithms in performance.

Inverse synthetic aperture radar (ISAR) is an effective detection method for targets. However, for the maneuvering targets, the Doppler frequency induced by an arbitrary scatterer on the target is time-varying, which will cause defocus on ISAR images, and bring difficulties for the further recognition process. It is hard for traditional methods to well refocus all positions on the target well. In recent years, generative adversarial networks (GAN) achieves great success in image translation. However, the current refocusing models ignore the information of high-order terms containing in the relationship between real parts and imaginary parts of the data. To this end, an end-to-end refocusing network, named Complex-valued Pix2pixHD (CVPHD) is proposed to learn the mapping from defocus to focus, which utilizes complex-valued (CV) ISAR images as input. A complex-valued instance normalization layer is applied to mine the deep relationship between the complex parts by calculating the covariance of them and accelerate the training. Subsequently, an innovative adaptively weighted loss function is put forward to improve the overall refocusing effect. Finally, the proposed CVPHD is tested with the simulated and real dataset, and both can get well-refocused results. The results of comparative experiments show that the refocusing error can be reduced if extending the pix2pixHD network to the CV domain and the performance of CVPHD surpasses other autofocus methods in refocusing effects. 1The code and dataset have been available online (https://github.com/yhx-hit/CVPHD).

Recently, researches on the classification for inverse synthetic aperture radar (ISAR) images continue to deepen. However, the maneuvering and attitude adjustment of space targets will bring high-order terms to received echoes which cause defocus on ISAR images and affect classification. The current classification models ignore the information of high-order terms containing in the relationship of real parts and imaginary parts of data. To this end, this letter proposes an end-to-end framework, called CV-GNN, specifically for the classification of defocused ISAR images under the few-shot condition. It models the features of real parts and imaginary parts of complex-valued (CV) images as graph information reasoning. Specifically, the deep relationship between them is mined to contribute to classification by complex-valued graph convolution. Moreover, the backpropagation process is derived in detail for updating the weights and bias of the network. The proposed method is then experimented with a mixed few-shot dataset of real and simulated data. Compared with the state-of-the-art methods, CV-GNN performs well in defocused image classification for each class of targets, and ablation studies verify the effectiveness of complex-valued network and graph neural network. The code and dataset will be available online (https://github.com/yhx-hit/cv_gnn).

The recognition of satellite's attitude change plays an important role in the detection, tracking and recognition of space targets, as well as the evaluation, verification of space events and environmental monitoring and prediction. In this paper, 3D-CNN model is used to extract features from spatial and temporal dimensions, and then 3D convolution is carried out to capture motion information from multiple consecutive frames. Four common attitude changes of three different kinds of satellites are simulated, which are orbit change, spin, reconnaissance and maneuver. A proper number of consecutive frames are sent into packets and sent to the network for training. The experimental result shows that 3D-CNN model has a competitive performance.

The intelligent production line is a complex application with a large number of independent equipment network integration. In view of the characteristics of CPS, the existing modeling methods cannot well meet the application requirements of large scale high-performance system. a formal simulation verification framework and verification method are designed for the performance constraints such as the real-time and security of the intelligent production line based on soft bus. A model-based service-oriented integration approach is employed, which adopts a model-centric way to automate the development course of the entire software life cycle. Developing experience indicate that the proposed approach based on the formal modeling and verification framework in this paper can improve the performance of the system, which is also helpful to achieve the balance of the production line and maintain the reasonable use rate of the processing equipment.