Against the backdrop of the design of electronic devices tending towards lightweight, low-profile, and multi-target features, a novel near-field multi-focus beam synthesis approach is put forward based on a new two-dimensional tensor impedance surface. On this basis, a tensor impedance surface that radiates near-field multi-focus beams is designed to operate at 10 GHz with a thickness of merely 1.524 mm. This surface accomplishes the radiation of multi-focus beams in space and the free proportioning of dual-polarization components of the multi-focus beams, and the multi-target energy convergence efficiency can exceed 40% at a distance of 1.5 m. Its low-profile design, straightforward feed structure, and multi-target radiation characteristics endow it with excellent application prospects in lightweight electronic devices.

In recent years, China's aerospace technology has continued to develop. The distance of deep-space exploration has been continuously increasing, and the attenuation of signal energy has become more and more serious, which has gradually increased the requirements for the signal reception and demodulation capabilities of ground receiving equipment. Antenna array technology can achieve the gain of an equivalent large-aperture antenna by synthesizing the signals of multiple small-aperture antennas, which can give full play to the efficiency of each antenna resource and achieve the purpose of extending the measurement and control distance. It is one of the effective ways to solve the problem of receiving weak signals in deep space. The measurement and control range of the antenna array of the ground fixed base station is limited by factors such as terrain, region, and obstructions, and it cannot achieve full coverage of the existing measurement and control tasks. Therefore, a shipborne platform is needed to make up for the measurement and control blind area. Based on the real satellite signal, this paper conducts an antenna array signal synthesis experiment on a shipborne mobile platform. For platform states such as docking at the wharf, anchoring on the river surface, and hull swaying, full-spectrum synthesis and symbol stream synthesis demodulation processing are carried out. Especially for the hull swaying state, a signal synthesis scheme based on carrier-to-noise ratio estimation is proposed. This scheme constructs a model of ship sway frequency, amplitude, and signal strength based on the measured ship sway data, analyzes the signal synthesis efficiency under different ship sway states, designs a carrier-to-noise ratio estimation scheme according to the signal change law, and dynamically optimizes the signal weighting coefficient, so as to improve the signal synthesis effect. The paper also compares the influence of different signal-to-noise ratio estimations and weighting coefficient update periods on the synthesis efficiency, and the results are consistent with the theoretical analysis. By comparing the synthesis efficiencies of the traditional signal synthesis scheme and the synthesis scheme based on carrier-to-noise ratio estimation, the test results show that the traditional symbol stream synthesis result is less affected by ship sway, and the synthesis efficiency can reach more than 90%; the traditional full-spectrum synthesis result is greatly affected by ship sway, and the synthesis efficiency is less than 80%. By adopting the carrier-to-noise ratio estimation-assisted scheme proposed in this paper, the synthesis efficiency can be significantly improved to more than 89%, providing a technical basis for the subsequent antenna array synthesis scheme of the shipborne mobile platform.

There are more and more applications of change detection methods based on deep learning in high-resolution remote sensing images. However, downsampling and cropping strategies deployed to fit the GPU (Graphic Processing Unit) memory constraints on processing large-size remote sensing images often result in incomplete semantic information and loss of fine details. In this paper, a collaborative supervised network based on feature pyramids is proposed to enable the network to learn local and overall features from cropped and downsampled image blocks. In addition, a feature-sharing mechanism is introduced to fuse global features and local features. We evaluated the network on the LEVIR-CD (a remote sensing change detection dataset) and S2Looking (a building change detection dataset) by comparing it with some representative change detection networks. The comparison experiments show that the proposed network performs better in multiscale change detection, with a 2.69% improvement in precision on LEVIR-CD, and 6.83% and 2.68% improvement in precision and recall on the S2Looking dataset, respectively.

This paper introduces an application of QC-LDPC codes to telemetry system. At the same time, a joint design method of code length and telemetry frame length is proposed. Spectrum resources, channel qualities, and hardware capabilities are comprehensively considered in this design method, so that the optimal scheme can be designed. By using the RU encoding algorithm and the LLR BP decoding algorithm, a hardware implementation method based on FPGA and AD936X architecture is proposed. The coding gain of the designed system is simulated according to the technical route. Combining the theoretical analysis and simulation results, it can be seen that of the advantages of low latency and high gain, the proposed telemetry system design method has certain application value in future telemetry systems. In summary, the proposed method solves the problem of high coding delay in traditional telemetry system, and brings about 8 dB coding gain at the cost of minimal delay.

X-ray communication technology is a kind of space communication mode using X-ray as a carrier, which has the advantages of a large communication bandwidth, light weight, small volume, low power consumption, and high confidentiality. By designing a multi-target X-ray signal modulation device, the X-ray communication rate can be improved, and the accuracy of X-ray energy spectrum recognition at the signal receiving end can be ensured, so that the advantages of X-ray communication based on energy load can be truly played. In this paper, an X-ray communication demodulation method based on peaking multi-level support vector machine is proposed to accurately identify the X-ray characteristic energy spectrum of multi-target materials. A peaking multilevel support vector machine classifier suitable for four-element communication is designed. The parameter tuning and verification ensure high accuracy and generalization ability. The simulation results show that support vector machine provides an efficient, accurate, and robust signal recognition solution for X-ray communication based on energy load.

In order to find a carrier-to-noise ratio estimation method with low computation and strong practicality, this paper draws on the experience of the classical variance summing method and proposes an improved algorithm. The distribution of the absolute value of the I-branch accumulative value is deduced when stably tracking GPS signals, and an expression for estimating the carrier-to-noise ratio is given based on its statistical characteristics. The Q-branch accumulative value is not necessary in this algorithm. From the aspects of estimation accuracy and computation, the improved algorithm is verified by simulation and actual measurement data analysis. The results show that the overall performance of the improved algorithm is equivalent, with an error of less than 0.4 dBHz, the stability is better than that of the classical variance summing method and narrow to wide power ratio method, and the computation is reduced by 50% compared to that the classical algorithm. The method has certain practicality in some receivers with limited hardware resources.

In recent years, as a crucial and fundamental task in applications such as autonomous driving, mobile robotics, and virtual reality, 3D object detection has received extensive attention from researchers in various fields. It aims to localize and classify objects of interest in 3D space and give the corresponding 3D bounding boxes, including the position, size, and orientation of objects, which provides the basic information for the subsequent understanding and perception of the 3D scene as well as planning and decision-making. Point clouds captured by LiDAR have become the most commonly used input data for 3D object detection due to their accurate 3D information and depth information. In this paper, the 3D object detection methods based on LiDAR point cloud with deep learning are reviewed, the characteristics and processing methods of point cloud are summarized, and several corresponding types of detection methods and multimodal fusion methods of point cloud and image are introduced. At the same time, this paper compares the performance of different methods and discusses the challenges and development trends of 3D object detection based on point cloud in the future.

The Phase Gradient Autofocus (PGA) algorithm is widely used to compensate for phase errors in Synthetic Aperture Radar (SAR) images. In the processing flow of the PGA algorithm, the two-step operation of selecting points and adding windows has a significant impact on algorithm performance. Traditional PGA algorithms often suffer from poor point selection quality or incorrect window width estimation, leading to poor focusing effect and slower convergence speed. This article proposes a strong point selection method based on the maximum energy signal-to-noise ratio criterion and an adaptive window width estimation method. Using the dimensions of energy and signal-to-noise ratio, ideal isolated strong scattering points are selected from image data, and two traditional windowing methods are combined and improved to adaptively estimate the window width, achieving improved algorithm stability and convergence speed. The simulation and experimental data processing results confirm the effectiveness of the algorithm proposed in this paper.

A CBAM-GRU classification model based on the combination of Convolutional Attention Mechanism Module(CBAM) and Gated Recurrent Unit (GRU) network is investigated for automatic modulation identification in non-cooperative communication systems. The pre-processed time-domain amplitude, phase and I/Q values of the signal are combined and converted into a matrix of input sample values, which are entered into the network for signal classification and identification. Simulations are conducted using the RadioML2016. 10a radio dataset, and the CBAM-GRU model are compared with the Convolutional Neural Network (CNN), Long Short-Term Memory network (LSTM), GRU, and Convolutional Long Deep Neural Network (CLDNN). The results indicates that the classification accuracy of the CBAM- GRU model reaches 92.79%, showing improvements of 8.52%, 1.84%, 1.75%, and 8. 61% over the comparison models respectively. Compared to traditional CNN or LSTM models, the CBAM-GRU model is more effective in capturing spatio-temporal features of sig- nals, thereby enhancing recognition accuracy.

To ensure the security of authentication and key distribution in the space information network, this paper proposes a public-key management scheme based on the elliptic curve cryptosystem. With the capacity for network access authentication and key agreement/update, this scheme can achieve various security goals, such as authenticity, anti-replay, and forward/backward security. For interstellar authentication and key agreement, our scheme only requires 3 interactions between the satellites. We provide a security and efficiency analysis for our scheme, and show that the proposed scheme not only satisfies the actual availability in the space information network, but also performs better than other existing public-key management schemes.