: Conventional wisdom portrays the People's Republic of China (PRC) People's Liberation Army (PLA) as a backward continental force that will not pose a military challenge to its neighbors or to the United States well into the 21st century. PLA writings that demonstrate interest in exploiting the revolution in military affairs (RMA) are dismissed by a large segment of the PLA-watching community as wistful fantasies. Major Mark A. Stokes, assistant air attache' in Beijing from 1992-1995, offers an alternative perspective. In this study, funded by the United States Air Force Institute for National Security Studies, he outlines emerging PLA operational concepts and a range of research and development projects that appear to have been heavily influenced by U.S. and Russian writings on the RMA. Fulfillment of the PLA's vision for the 21st century could have significant repercussions for U.S. interests in the Asia-Pacific region. Major Stokes ventures into facets of PLA modernization that are often ignored. Backed by extensive documentation, he argues that the revolutionary modernization of the PRC's telecommunications infrastructure, a robust space-, air-, and ground-based sensor network, and prioritization of electronic attack systems could enable the PLA to gain information dominance in future armed conflicts around its periphery. Information dominance would be further boosted by China's traditional emphasis on information denial and deception.

/pdf/china-s-strategic-modernization-implications-for-the-united-39rbjcd9vk.pdf

China's Strategic Modernization: Implications for the United States

The Chinese SLR Network consists of 5 fixedstations located in Shanghai, Changchun, Beijing, Wuhan and Kunming respectively and 2 mobilesystems. The single shot precision for Lageos for these stations is in therange of 12–30 mm. Improvements of system performance for all stations are under way.The Chinese SLR Network participates in the national project “CrustalMovement Observation Network of China”, and is actively supporting the global and regionalprojects, such as ILRS, IERS, WPLTN (Western Pacific Laser Tracking Network) andAPSG (Asia-Pacific Space Geodynamics). The operation center and the datacenter for the Chinese SLR Network is located at Shanghai Observatory. The Lageos dataanalysis report for the global stations has been published every two weeks at the web siteof Shanghai Observatory since September 1999.

Current Status And Future Plans For The Chinese Satellite Laser Ranging Network

The issues of China's nuclear command, control and operations are a secret dominion and lack serious discussions due to the dearth of available literature. In this article, the author argues that China's command, control and communications systems suffer technological problems, despite the efforts being made. Owing to technological progress, China's strategic and tactical nuclear targetting plans are versatile, but its current striking capability is insufficient. The potential use of tactical nuclear weapons, which China has been developing for decades, could contradict China's no first use commitment. In a nutshell, the author is convinced that China's nuclear posture is more aggressive than its oft-cited policies, and the Chinese are constantly preparing for a more offensive nuclear posture against other nuclear powers, although China's nuclear operational capability is still far behind.

China's nuclear command, control and operations

Correction: Chen et al. HP3D-V2V: High-Precision 3D Object Detection Vehicle-to-Vehicle Cooperative Perception Algorithm. Sensors 2024, 24, 2170

Collaborative perception exploits exchanging perception data across multiple agents to enhance situational awareness. To overcome the constraints created by limited network resources, it is necessary to have a reliable collaborative perception that can sustain the trade-off between performance and bandwidth. This research proposes a method to address this issue through the development of a graph attention network (GAT) for intermediate collaborative perception. The proposed approach aims to enhance object detection accuracy by exchanging information among multiple agents, addressing issues such as sensor limitations, and occluded objects, and expanding the sensing range. Additionally, the proposed approach addresses the challenges caused by limited communication bandwidth and inconsistencies in the data shared by different agents by maintaining a balance between performance and bandwidth, fitting real-life application requirements. Our approach aggregates the intermediate features obtained from multiple neighboring agents and introduces a novel attention mechanism to selectively emphasize significant regions within the intermediate feature maps. This attention mechanism operates on both the channel and spatial levels to direct the data aggregation process. This research presents a new method for aggregating features utilizing various attention architectures. We perform quantitative and qualitative assessments of the final results of this method and compare them to other state-of-the-art collaborative perception techniques. The results of this work are evaluated using the V2XSim and OPV2V datasets, and our quantitative and qualitative experiments in multi-agent object detection show that our approach achieves a better performance than the state-of-the-art collaborative perception methods.

CollabGAT: Collaborative Perception using Graph Attention Network

Cooperative perception in the field of connected autonomous vehicles (CAVs) aims to overcome the inherent limitations of single-vehicle perception systems, including long-range occlusion, low resolution, and susceptibility to weather interference. In this regard, we propose a high-precision 3D object detection V2V cooperative perception algorithm. The algorithm utilizes a voxel grid-based statistical filter to effectively denoise point cloud data to obtain clean and reliable data. In addition, we design a feature extraction network based on the fusion of voxels and PointPillars and encode it to generate BEV features, which solves the spatial feature interaction problem lacking in the PointPillars approach and enhances the semantic information of the extracted features. A maximum pooling technique is used to reduce the dimensionality and generate pseudoimages, thereby skipping complex 3D convolutional computation. To facilitate effective feature fusion, we design a feature level-based crossvehicle feature fusion module. Experimental validation is conducted using the OPV2V dataset to assess vehicle coperception performance and compare it with existing mainstream coperception algorithms. Ablation experiments are also carried out to confirm the contributions of this approach. Experimental results show that our architecture achieves lightweighting with a higher average precision (AP) than other existing models.

HP3D-V2V: High-Precision 3D Object Detection Vehicle-to-Vehicle Cooperative Perception Algorithm

A satellite laser ranging system with an accuracy of the level of centimeter has been successfully developed at the Institute of Seismology, State Seismological Bureau with the cooperation of the Institute of Geodesy and Geophysics, Chinese Academy of Science. With significant improvements on the base of the second generation SLR system developed in 1985, ranging accuracy of the new system has been upgraded from 15 cm to 3-4 cm. Measuring range has also been expanded, so that the ETALON satellite with an orbit height of 20,000 km launched by the former U.S.S.R. can now be tracked. Compared with the 2nd generation SLR system, the newly developed system has the following improvements. A Q modulated laser is replaced by a mode-locked YAG laser. The new device has a pulse width of 150 ps and a repetition rate of 1-4 pps. A quick response photomultiplier has been adopted as the receiver for echo; for example, the adoption of the MCP tube has obviously reduced the jitter error of the transit time and has improved the ranging accuracy. The whole system is controlled by an IBM PC/XT Computer to guide automatic tracking and measurement. It can carry out these functions for satellite orbit calculation, real-time tracking and adjusting, data acquisition and the preprocessed of observing data, etc. The automatization level and reliability of the observation have obviously improved.

/pdf/new-progress-of-ranging-technology-at-wuhan-satellite-laser-1ro47somr8.pdf

New progress of ranging technology at Wuhan Satellite Laser Ranging Station

Solar disk velocity difference is a novel celestial navigation measurement, which can be obtained by four spectrometers installed on the four corners of the quadrangular pyramid. However, the characteristic of the spacecraft’s high dynamic results in the spectrometer installation error. Spectrometer installation error has an obvious influence on navigation accuracy. To solve this problem, an augmented state sun direction/solar disk velocity difference integrated navigation is proposed. After analyzing the influence of spectrometer installation error on the solar disk velocity difference measurement, the spectrometer installation error is augmented into the state vector. The state model and measurement model of the augmented state sun direction/solar disk velocity difference integrated navigation considering spectrometer installation error are established. UKF is used to estimate and correct spectrometer installation error. Simulation results show that the proposed method greatly suppresses the effect of spectrometer installation error on navigation accuracy. Furthermore, the effects of the sun sensor accuracy, the sample time, the number of spectrometers, and the spectrometer installation error value on the navigation performance are analyzed. 

A Novel Sun Direction/Solar Disk Velocity Difference Integrated Navigation Method Against Installation Error of Spectrometer Array

This article proposes a lightweight multimodal fusion object detection algorithm based on bird’s eye view (BEV) perception, addressing the challenges of high computational complexity and insufficient feature fusion when integrating camera and light detection and ranging (LiDAR) data in autonomous vehicles. To alleviate the computational burden, depthwise separable convolution and large separable kernel attention (LSKA) are introduced, constructing a lightweight feature extraction network that effectively captures features from camera data. To enhance computational efficiency, a parallel-optimized BEV pooling structure is proposed that improves the computation process and memory access patterns. In the feature fusion stage, a novel dualmodality dual-attention feature fusion module is designed that integrates the features from both modalities using parallel channel attention and spatial attention mechanisms to strengthen correlations between multimodal features. In addition, a weight generation network is introduced to adaptively assign fusion weights to features of the two modalities. The proposed algorithm has a lightweight structure and achieves cross-modal feature alignment in the BEV space. The experimental results of the public nuScenes dataset show that the proposed algorithm achieves an mAP of 0.682 and a normalized discounted success (NDS) of 0.710, while maintaining detection accuracy similar to the baseline model. Furthermore, the algorithm reduces the computational load from 253.2 to 202.3 G MACs, approximately a 20% decrease, and improves the inference speed from 8.4 to 10.0 FPS. Furthermore, our algorithm has also been experimentally validated on theWaymo dataset, demonstrating performance comparable to that of the BEVFusion method. These results demonstrate significant improvements in both computational efficiency and deployment-friendliness while preserving detection performance.

A Lightweight Multimodal Fusion Method for Object Detection Based on Bird’s Eye View

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