Aviation network nodes have the characteristics of wide area distribution and high dynamics, and the mobile model directly affects the accuracy and reliability of aviation network simulation. In this paper, on the basis of the existing mobile model of Ad-Hoc network, a mobile model of aeronautical self-organizing network based on platform attribute and mission type is proposed. By matching node attributes to mission and mission to mobile model, the relationship between nodes and mobile model is established, and the mobile model adaptation solution for different aircraft platforms in different mission phases is proposed. A UAV mission scenario was constructed to analyze the feasibility of the mobile model.
KEYWORDS: Defense and security, Monte Carlo methods, Control systems, Defense systems, Computer simulations, Missiles, Weapons, Target acquisition, Defense technologies
The integrated and coordinated operation of warships and aircraft is the core issue of air-sea battle control. Ship antiaircraft and carrier-based aircraft are vigilant in different airspaces. In the face of uncertain threats, the defense effects of ships and aircraft are different, and in order to avoid the overlap between ship firepower and aircraft firepower, The assignment of goals becomes very important. Based on the zoning principle of warship and aircraft cooperative defense, this paper proposes a target attack time calculation method for combat situation. By calculating the estimated time of the attack and threat of the ship and the aircraft at the current position as the estimated interception time, factors such as the maneuver time of the aircraft, the flight time of the weapon, and the interception area need to be considered. Based on this method, the target grouping algorithm with minimum target intercept time is given. According to the algorithm, the warship-to-aircraft cooperative air defense is realized. The Monte Carlo simulation analysis is carried out on the two combat situations in the dynamic combat simulation platform. By comparing the simulation results, we find that the proposed algorithm can effectively reduce the damage rate of the warship.
The unmanned aerial vehicle resources include subsystem resources such as sensors, weapons and communication links, in order to meet the resource scheduling and management requirements of unmanned aerial vehicle, this paper studies the resource scheduling and management mechanism of unmanned aerial vehicles from the aspects of demand analysis, functional framework and operation process. It mainly analyzes the functions and operation flow of unmanned aerial vehicle, including resource adaptation conversion, access control, access control, resource state maintenance, etc., so as to provide references for the packaging and adaptation of unmanned aerial vehicles resources and the design of resource management software.
In the all-time matching and navigation task, the aircraft applies real-time infrared images acquired by infrared imagery sensor to match the referenced visible image provided by the satellite for accurate location. However, the large difference between the infrared image and the visible image makes the task challenging. In this paper, for the sake of engineering application in the avionics system, we obtain real-time infrared images according to the flight trajectory, and then use them to match the referenced visible images. Furthermore, the HOG features are extracted respectively from real-time infrared images and referenced visible images to describe their feature similarity, for the purpose of accurate matching and localization. Experimental results demonstrate that our proposed method can not only realize the matching between airborne infrared and visible images, but also achieve high location accuracy, which shows good performance and robustness.
Ship detection from optical images taken by high-altitude aircrafts such as unmanned long-endurance airships and unmanned aerial vehicles has broad applications in marine fishery management, ship monitoring and vessel salvage. However, the major challenge is the limited capability of information processing on unmanned high-altitude platforms. Furthermore, in order to guarantee the wide detection range, unmanned aircrafts generally cruise at high altitudes, resulting in imagery with low-resolution targets and strong clutters suffered by heavy clouds. In this paper, we propose a low-resolution ship detection method to extract ships from these high-altitude optical images. Inspired by a recent research on visual saliency detection indicating that small salient signals could be well detected by a gradient enhancement operation combined with Gaussian smoothing, we propose the facet kernel filtering to rapidly suppress cluttered backgrounds and delineate candidate target regions from the sea surface. Then, the principal component analysis (PCA) is used to compute the orientation of the target axis, followed by a simplified histogram of oriented gradient (HOG) descriptor to characterize the ship shape property. Finally, support vector machine (SVM) is applied to discriminate real targets and false alarms. Experimental results show that the proposed method actually has high efficiency in low-resolution ship detection.
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