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.

Digital channelization technology is often used for broadband electromagnetic signal reception. When its analysis filter banks and comprehensive filter banks have precise reconstruction characteristics,accurate reconstruction of the received signal can be achieved. For the electromagnetic spectrum recognition problem in the field of complex electromagnetic environment perception,it is necessary to accurately restore the electromagnetic signal received by the receiver and perform spectrum recognition based on the accurately restored signal. The article proposes an intelligent electromagnetic spectrum recognition technology based on precise reconstruction of digital channelization. Firstly, a digital channelized receiver structure that can achieve precise signal reconstruction is constructed. Then,wavelet analysis is used to construct a time-frequency waterfall diagram of the signal,and artificial intelligence processing is performed based on this graph to achieve electromagnetic spectrum recognition. Finally,simulation results are provided. The simulation results in the article demonstrate the correctness and effectiveness of the method.

Image fusion model based on autoencoder network gets more attention because it does not need to design fusion rules manually. However, most autoencoder-based fusion networks use two-stream CNNs with the same structure as the encoder,which are unable to extract global features due to the local receptive field of convolutional operations and lack the ability to extract unique features from infrared and visible images. A novel autoencoder-based image fusion network which consist of encoder module, fusion module and decoder module is constructed in this paper. In the encoder module, the CNN and Transformer are combined to capture the local and global feature of the source images simultaneously. In addition, novel contrast and gradient enhancement feature extraction blocks are designed respectively for infrared and visible images to maintain the information specific to each source images. The feature images obtained by encoder module are concatenated by the fusion module and input to the decoder module to obtain the fused image. Experimental results on three datasets show that the proposed network can better preserve both the clear target and detailed information of infrared and visible images respectively, and outperforms some state-of-the-art methods in both subjective and objective evaluation. Meanwhile, the fused image obtained by the proposed network can acquire the highest mean average precision in the target detection which proves that image fusion is beneficial for downstream tasks.

Satellite resources on board are limited and precious, and ground terminals in real communication scenarios have different geographic distribution and satellite communication service demands, so dynamic on board satellite resource management is needed to design flexible and efficient resource management schemes. In this paper, the use of hopping beam technology can provide flexible resource management at the beam level to solve the problem of unbalanced service demand between ground cells covered by point beams generated by high-throughput satellites. Firstly, in order to solve the problem of co-channel interference in beamhopping satellites, an interference avoidance strategy between beam-hopping clusters based on frequency mode switching is proposed. Further, two cluster configuration strategies, uniform and non-uniform clustering, are proposed. In order to solve the problem of poor equalization effect of uniform clustering and unsuitable for the dynamic change of the ground, combined with the problem of co-channel interference, a non-uniform cluster configuration strategy based on frequency mode switching is proposed for the interference avoidance strategy between beam-hopping clusters. Finally, the interference avoidance strategy is verified and simulated. Besides,the different clustering strategies are verified based on various intelligent optimization algorithms. The simulation results verify the feasibility of the clustered configuration and show that the non-uniform clustering algorithm with no distance limitation has the strongest flow-balancing ability.

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.

According to the application environment of deep space laser communication, this paper analyzes the key performance indicators in the communication link. After taking into account factors such as distance spot diffusion attenuation, pointing control accuracy attenuation, atmospheric attenuation, solar radiation, detector dark current noise etc., this paper determines the specific calculation method of communication rate and communication bit error rate. Through formula derivation, the impact of the transmitter telescope diameter (laser beam divergence angle) and pointing control accuracy on the maximum communication distance is analyzed, and numerical simulation is used to support and further analyze the constraint relationships between transmitter telescope diameter (laser beam divergence angle) and pointing control accuracy and proposed design methods for the design of deep space laser communication links. The performance of laser links in the scenarios of earth-moon space and earth-Mars space under the existing laser communication capabilities is further calculated.

Aiming at the problem that the Siamese network has insufficient ability to express the features of scale-varying targets, a multi-branch structure is constructed by using convolution, pooling branches and pruning operations of different sizes to improve the robustness of features and ensure the translation invariance of the Siamese network. Aiming at the problem that the multi-branch structure brings too many parameters, the multi-branch structure is reparameterized into a single convolution in the tracking stage, which effectively reduces the time cost in the tracking stage. The experimental results show that compared with SiamFC, the accuracy, success rate and tracking speed of the proposed algorithm on the OTB100 datasets are improved by 5.1%, 3% and 30 FPS,respectively. The tracking accuracy and success rate are improved on the UAV123 and Temple-Color-128 datasets.

In recent years, with the development of new models of China's spacecraft and the advancement of the localisation process, considering the demand for highly integrated and remotely dynamically reconfigurable telemetry equipment, the development of remotely dynamically reconfigurable telemetry encrypted transmitters compatible with multiple modulation regimes is an inevitable trend in the development of telemetry transmission equipment. Differing from the traditional transmitter baseband software architecture of FPGA+DA+channel approach, the transmitter architecture consists of a heterogeneous multicore FMQL embedded system+RF integrated chip B9361 to form the transmitter baseband. This is combined with the software to achieve the functions of remote dynamically reconfigurable, poweron self-test, working status patrol, and real-time reporting of the working status. This improves the degree of integration of the measurement system and realizes the reconfiguration and sharing of the software and hardware resources to the maximum extent.It also significantly reduces the system volume, weight, power consumption, and cost. This scheme is based on the design ideas of generalization, serialization, and combination, so that the transmitter product has the advantages of high integration, serialization, and controllable components, generalized platform, flexible software configuration, etc. This provides strong technical support for the development of the new generation of telemetry transmitters in the direction of generalization, high performance, and dynamic reconfigurability.

In this paper, an improved Surface Acoustic Wave (SAW) temperature and pressure sensor with two chambers is proposed to solve the nonlinear coupling problem in the integrated structure suggested in earlier studies, and the design and experimental study of an acoustic surface wave all quartz pressure sensor is carried out. Based on the finite element method and perturbation theory, the response mechanism of the quartz-based SAW pressure sensor is analyzed, the coupled mode theory is used to optimize the design of the three-resonator-type sensitive element, and the glass paste bonding is used to realize the quartz cross-lead hermetic encapsulation, and the preparation of the SAW allquartz pressure sensor is realized. The test results show that the developed SAW all quartz pressure sensor has a pressure range of 0~500 kPa, a linearity of 0.415% FS, a pressure sensitivity of 551 kHz/MPa, and a temperature coefficient of sensitivity of 0.134% over the operating temperature range of 0 ℃~120 ℃. The development of this surface acoustic wave all quartz pressure sensor lays the foundation for the subsequent realization of wireless passive measurement of the sensor.

The reliability, security, and real-time of information transmission are crucial in defence application. Aiming at the problems that the useful information required cannot be accurately received in the battlefield environment with complex and strong electromagne-tic interference as well as multi-module integration, this paper creates a function, calculates the optimal value of the parameters through Monte Carlo experiments, and proposes a variable step size LMS adaptive beam forming algorithm based on elliptic function. Compared with other algorithms, the results show that the proposed algorithm is superior to the existing variable step size LMS algorithm in convergence speed under the premise of ensuring steady-state error, and the computational complexity is small. By simulating the interference signals of different incoming wave directions to draw the beam direction map, the simulation results show that this paper’s algorithm pointing strong, high interference suppression system, can achieve the effective reception of useful signals, effective suppression of interfering signals and equipment integration and miniaturization of the demand.