Search

Aiming at the target search task, considering the existing problems of unreasonable initial distribution of UAVs and poor consistency of start and end time of swarm tasks in the existing coverage path planning algorithms, this paper designs an equal-time tendency region segmentation algorithm based on the initial scenario of realistic UAV cluster centralized deployment. The optimization goal of the algorithm is to minimize the maximum waiting time of individuals in the UAV swarm, and the task time of the UAV is affected by changing the size of the UAV search area. The algorithm has a secondary structure. The first level is the initial coarse segmentation, which solves the problem of too many iterations of boundary points. In the second level, the task time deviation value is used as the adjustment value to ensure the unity of task time. The simulation results show that the algorithm designed in this paper is more suitable for the scene of concentrated UAV delivery. The algorithm successfully reduces the task waiting time between UAV individuals. It facilitates the secondary scheduling of resources and the synchronous execution of multi-order tasks. [ABSTRACT FROM AUTHOR]

This paper discusses the reliability deployment of Service Function Chain (SFC) on the hybrid satellite network based on SDN architecture. Firstly, this paper describes the reliability protection problem of SFC, establishes the underlying network and SFC request model, and then establishes the reliability requirement model of network service function and the reliability requirement model of LEO satellite link, and clarifies the optimization goals and constraints. Then, a reliability-based satellite service function chain protection method is proposed, including a reliability protection algorithm based on deep reinforcement learning and a reliability backup algorithm based on LEO satellite node and link. Experiments show that the proposed reliability-based satellite service function chain protection method can improve the SFC request acceptance rate, reduce the average delay, and maintain high request acceptance on hybrid satellite networks based on SDN architecture. [ABSTRACT FROM AUTHOR]

For the lack of command and control modeling principles and mechanisms in the current end-to-end campaign tactical level simulation for unmanned air combat, this paper systematically combs and studies the recent research in unmanned air campaign tactical simulation. Combined with the relevant research, this paper explored the principles and methods of different C2 modeling at different simulation levels and established a C2 simulation system suitable for the characteristics of CISE in unmanned air combat. Further, theoretical and practical guidance is provided for innovative demonstration and evaluation methods of CISE through the effectiveness evaluation support of unmanned air combat tasks. [ABSTRACT FROM AUTHOR]

In the ship information big data system, aiming at the problems such as long time consuming, distortion of thermal map effect and poor tracking or lag in the roaming of map software when the client requests a large amount of historical data from the server and draws heat map in real time, this paper proposes an improved real-time generation strategy of map map of ship history activity. Based on the thermal map generation method with the idea of pixel clustering, this strategy uses the image enhancement algorithm based on nonlinear transformation and pixel complement method to correct and optimize the display distortion problems existing in the head map. On this basis, the dynamic loading and drawing strategy and multi-thread real-time mapping strategy are proposed to generate the heat map of the ship’s historical activities. Experiments show that the strategy proposed in this paper has been improved in the efficiency of heat map drawing, mapping effect and interactive experience of map software. [ABSTRACT FROM AUTHOR]

In onder to scientifically evaluate the command capability of a joint combat command organization, the paper establishes a command capability indicator system for the joint combat command organization, analyzes the interrelationships between indicators at various levels and the interrelationships between elements within the indicator group, and uses Super Decisions software to establish a relationship diagram of command capability evaluation indicators for the joint combat command organization, obtaining the weight of the final indicator system, collects two groups of command organization exercise evaluation data and calculates the evaluation results of capability. By analyzing the weight of indicators, it is pointed out that to improve the command capability of joint operations organizations, it is particularly important to focus on three key points: planning and decision-making capabilities, intelligence reconnaissance capabilities, and operational control capabilities. [ABSTRACT FROM AUTHOR]

To solve the problems of unstructured data organization, insufficient detailed description of entity relationship and lack of standardized expression of knowledge representation in the construction of knowledge graph in the field of early warning equipment. Based on the Protégé ontology construction tool, this paper divides the early warning equipment system from top to bottom into equipment principle knowledge class, management support resource class, equipment operation and application class, etc., and then refines them. So as to define the concept class and divide the hierarchical relationship, and extract the entities, attributes, relationships and other knowledge units in specific equipment information. Then, the domain knowledge of early warning equipment is modeled, and a scientific and complete knowledge representation framework of finegrained early warning equipment is explored. Taking "long-range early warning phased array radar as an example, ontology instance filling and visualization are carried out. Finally, the ontology of early warning equipment is formally represented based on ontology description language. The ontology lays a semantic foundation for the construction of domain knowledge graph based on multi-source high-quality data, and has important practical guiding value for realizing intelligent management support and operational application of equipment such as equipment capability portrait, development trend analysis, equipment fault diagnosis, and health status management under the background of big data. [ABSTRACT FROM AUTHOR]

In response to the existing gun sound recognition and positioning tasks, which require separate identification and positioning, resulting in time-consuming computation, system redundancy, and complex development processes, this paper proposes to use a two-stage CRNN deep learning network model to complete the gun sound recognition and positioning tasks. Firstly, perform a logarithmic Mel transform on the collected gunshot signal and calculate the generalized phase transition cross correlation spectrum as input to the network model. Secondly, in the first stage, the gunshot signal is identified through the CRNN network. Finally, in the second stage, the introduction of a mask is used to determine whether the CRNN network weight sharing is implemented for localization. The method proposed in this article can effectively solve the problems of separate recognition and positioning tasks, system redundancy, and complex development processes in traditional methods, and has certain application value in achieving joint recognition and positioning. [ABSTRACT FROM AUTHOR]

The research emphasized the invulnerability of tree command network and Cascade Command network based on the normal duty command model. Random attack and deliberate attack are employed on three kinds of duty command models. The paper found out the best anti-destruction performance model by comparing and analyzing indicators like the indexes of maximum connectivity subgraph size, network efficiency, global aggregation coefficient ete. The collective influence algorithm is used to identify the key nodes of the command network with the best anti-destruction performance, and compared with the traditional key node identification method based on node degree, it is proved that it is more accurate to identify key nodes based on the collective influence algorithm which could provide theoretical guidance for the optimization and protection of the future regular duty command network. [ABSTRACT FROM AUTHOR]

Frequency Diverse Array (FDA) is a brand-new electronically scanned radar system. The time-range-angle three- dimensional correlation characteristics of its beampattern have broad application prospects in the fields of electronic counter- measures and security communications. One-dimensional uniform linear FDA array (ULFDA) has the problem of angle-range coupling in the transmit beampattern. To solve this problem, this paper adopts sine based on the symmetrical subarray density taper array structure. The frequency offset obtains the dot-shaped beampattern pointing control method at the target point posi- tion. In addition, we analyze the time-varying problem of the FDA pattern, and get the conclusion that the mainlobe of the density tapered array sin-FDA based on the subarray structure has a fixed direction in the angle dimension and changes in the range dimension. [ABSTRACT FROM AUTHOR]

Aimming at the multitudinous battlefield situation information and the difficulty in recognizing the changing trends, based on large models, a situation awareness agent and an intelligent situation awareness inductive method are proposed. Starting from cognitive concepts and combining the abstractness and embodiment characteristics of agents, three key components in the construction of agents have been clarified: learning environment, memory mode, and knowledge generation mechanism. The architecture of the battlefield situation awareness agent is designed, including memory component, planning component, execution component, evaluation component, and key points for agent training. In the long-term memory component, based on the modeling characteristics of complex battlefield states, the paper discusses the application of large language models, multimodal large models and large sequence models. [ABSTRACT FROM AUTHOR]

Aiming at the difficulties in the quantitative verification and evaluation of underwater countermeasures for our underwater platform and the lack of a guided maneuvering evasive combat model, a prediction model of underwater countermeasures based on big data learning is designed. Firstly, the underwater countermeasure modeling of the underwater platform is carried out, and the large data set of avoidance probability is obtained by several rounds of simulation based on Monte Carlo method. At the same time, in order to solve the problem of poor time efficiency under massive simulation, BP neural network prediction algorithm is proposed for big data learning to provide accurate, fast and visual antagonistic results. The test results show that under the test environment set in this paper, the average prediction error of BP neural network prediction algorithm is 7. 28% respectively, which can effectively predict the avoidance probability of the underwater platform and provide data support for the commander's command decision. [ABSTRACT FROM AUTHOR]

In this paper, aiming at the problem that the explosion belt is offset by wind during the operation of rocket explosive device, the dynamic model affected by wind and launching angle in different environments are established. The relationship between wind speed, wind direction and launching angle of rocket explosive device is analyzed and studied, and the model is simulated and verified by combining the relevant data. The model can provide method support for rational use of rocket explosive device and improvement of operational efficiency under actual combat conditions. [ABSTRACT FROM AUTHOR]

The operational application of Starlink system is becoming more and more mature. Considering that Starlink system is still under rapid construction, it is necessary to conduct a systematic analysis of its future operational application. In order to explore the future operation mode of the Starlink system of the US military, this paper, based on the analysis of the fundamental state of Starlink, focuses on high-end wars of great powers, explores the impact of Starlink on the operations of the US military from four levels of operational theory, and shows the system supporting role of Starlink. Finally, an operational simulation experiment of Starlink is given to analyze the long-term capability. By analyzing the operational mode and capability of the mid-stage Starlink operation, it lays a foundation for the follow-up countermeasures. [ABSTRACT FROM AUTHOR]

Aiming at the comprehensive application of marine relay equipment, combined with the maximum communication distance and target threat distribution and other conditions, this paper proposes an programming model to solve the optimal deployment of communication relay platform array. This model is based on the principle of the shortest distance between the route starting point and the array postion point of the relay platform. It models the array deployment problem of the relay platform under the constraints of communication distance and target threat, and uses the convex optimization method to obtain the analytical form of the optimal solution. The optimal solution has strong interpretability, and the solution process avoids using traditional iterative or heuristic methods, which has the characteristics of small calculation. The example analysis shows that the optimal solution of the array deployment can be obtained quickly for the different position distribution of the mission platform and the threat target. [ABSTRACT FROM AUTHOR]

The cooperative region search of UAV swarm can obtain ground information of the mission region and reduce the uncertainty of environmental information effectively. The traditional collaborative region search methods based on the balanced allocation of divided region and the heuristic algorithms depend on the pre-designed rules and heavy computation, and have no ability to generate new rules of the cooperative search. These algorithms belong to the algorithms that can not evolve new rules. Due to the complexity of the mission environment, the algorithms must contain fast,intelligent and robust characteristics, the cooperative searching algorithms of UAV swarm based on emerging theory with strong information fusion ability, self-learning ability have been widely concerned. Evolutionary and reinforcement learning algorithms are the important parts of the emerging theory and both of them can generate some new cooperative searching rules according to the different environment and task. The paper would systematically analyze and summarize the current research status and progress of cooperative search methods. Finally, the shortcomings of the existing research and the further development are put forward. [ABSTRACT FROM AUTHOR]

The Manned-Unmanned Teaming technology can fully leverage the low cost and expendable advantages of unmanned platforms to expand the operational space of manned platforms, thereby enhancing their situational awareness, penetration capability, and survivability. This paper provides a research overview on the development of MUM-T in the United States, Britain, France, Australia, South Korea, and other countries. It provides several application examples of MUM-T in foreign militaries on marine, land, and air space. It also analyzes some technical challenges and key technologies of MUM-T in wireless communication and networking, remote measurement and control, autonomous decision-making, and human-machine interaction. Finally, the future development trend of MUM-T is prospected, which can serve as a reference for the study of collaborative combat styles and related technological research in future joint operational systems. [ABSTRACT FROM AUTHOR]

Multi-UAV cooperative regional search is widely used in military and civil, such as search, rescue, patrol, monitoring, and environmental survey. It is an open topic to enhance the efficiency of target search. The paper proposes a probability calculation method based on UAV track elimination strategy and policy iteration on a target region without obstacle to improve the efficiency of UAV cooperative search based on prior information. The track elimination strategy is measured by the track of the UAV and the prior information, which include detectivity of sensor as well as heat power of target region. Then heat power on the track of a single UAV is refreshed, and the track is dynamically planed by policy iteration algorithm. On the basis of the method, the tracks of all UAVs are calculated one by one with appropriate order. Finally, the effectiveness of the algorithm is verified by simulation. [ABSTRACT FROM AUTHOR]

In view of the catastrophic forgetting of previous knowledge in class incremental learning for image classification, existing replay-based methods focus on memory updating and sampling, while overlooking the feature relationships between old and new samples. To this end, the paper proposes a method called contrast metric enhancement based on memory extraction(cME2) for Online Class-incremental Learning in Image Classification, which designs two new types of positive and negative sample-pairs, enhances the reuse of old sample feature information, and strengthens the ability of model to express redundant features and common features. It improves the distribution of samples in embedding space based on the nearest class mean classifier. Finally, the effectiveness and efficiency of the proposed method are verified by comparison experiment and ablation experiment. [ABSTRACT FROM AUTHOR]

This paper proposes an improved adaptive noise-aided complete ensemble empirical mode decomposition (ICEEMDAN) method to address the issue of low signal-to-noise ratio in distributed optical fiber acoustic sensing systems. The proposed approach utilizes sample entropy and wavelet threshold denoising algorithm to extract valuable components from high noise components. The ICEEMDAN is applied to decompose the acquired signals, and sample entropy is calculated to identify the noisy components, which are then subjected to wavelet threshold denoising. Finally, the denoised components are reconstructed with the untreated intrinsic mode functions. Experimental results demonstrate that the denoising treatment significantly enhances the signal-to-noise ratio by 5.34 dB, reduces the mean square error by 0.014 8, and improves waveform similarity by 5.7%. Compared to other commonly used denoising methods, the proposed approach not only exhibits superior performance in terms of signal-to-noise ratio but also demonstrates better performance in mean square error and waveform similarity, thereby preserving useful signals more effectively. [ABSTRACT FROM AUTHOR]