A three-dimensional reconstruction algorithm based on line laser rotation scanning is proposed. The rotating platform vision measurement system is designed. The system calibration method consisting of laser, camera and turntable is studied. The axis-eye calibration algorithm is introduced to register the point cloud in the same coordinate. In the system, the camera driven by the turntable is rotated to scan the target object for three-dimensional measurement. The method can obtain the point cloud information of the object by linear laser rotation scanning, and further recover the geometric shape of the three-dimensional visible surface of the object, which not only meets the requirements of the three-dimensional reconstruction target, but also verifies the feasibility and effectiveness of the system.

For deep sea equipment suspension installation used in marine engineering, the multi-camera video motion analysis method is used to calculate the three-dimensional underwater trajectory of the underwater engineering structure. Considering difficulty in underwater modeling caused by the problems of light scattering and refraction under water, the camera imaging model on land is no longer applicable in water, and a new underwater camera imaging model needs to be proposed. This paper introduces an underwater camera imaging model with light refraction, studies the calibration method of the internal and external parameters of the underwater camera, and improves the multiscale rotation dense feature pyramid convolutional neural network to detect the position of the target object in the image. The underwater motion videos of the target produced by three fixed underwater cameras are optimized to fuse and calculate the trajectory of the underwater target. This method is suitable for large-scale motion of underwater objects and can obtain more accurate trajectories. Experimental analysis and data comparison have verified the effectiveness of the method.

A weapon system-of-systems (WSoS) is a higher-level system comprising various functional weapon equipment systems interconnected via mutual relationships, forming a hierarchical structure that can generate overall combat effectiveness. A critical factor in assessing WSoS performance is the kill chain, and quantifying the combat effectiveness of a WSoS based on the kill chain is crucial for optimizing the system’s structure and improving the understanding of the battlefield situation, holding significant military value. Scenarios involving restricted communication (e.g., limitations in weapon system capabilities, terrain obstructions, or enemy interference) make analyzing WSoS performance challenging, so proposed here is a kill chain-based method for analyzing WSoS capability in order to address the impact of communication restrictions. Specifically, a generalized multilayer network model with information relays is used to network the WSoS, then based on this, a capability-matrix-based method for generating and analyzing the kill chain is designed. Experiments show that the proposed model and method enable effective generation and analysis of the kill chain in communication-denial situations. Furthermore, a framework for evaluating WSoS performance is established from the dimensions of mission tasks and network structure, and combat effectiveness is assessed by quantifying performance indicators based on kill chain information. Finally, case studies are used to validate the proposed algorithm and show its reliability.

The weapon system-of-systems is an assemblage of diverse weapon systems designed to accomplish shared objectives and missions, and harnessing it to dominate the battlefield is pivotal to securing victory. Relaying battlefield information faces challenges under communication constraints, and this makes it very difficult to construct a networked model of a weapon system-of-systems and close the operational links. To achieve effective kill chains within weapon equipment systems, this study investigates a double relay directed hypernetwork model based on an adjacency matrix. First, a dual-layer relay network integrating perceptual and decision-making information is introduced, using relay nodes to establish indirect transmission of reconnaissance and command information. Furthermore, a breadth-first redundancy filtering search algorithm is developed to identify optimal paths for information transmission within the relay network, serving as a bridge to generate all kill chains that meet the imposed constraints. Consequently, kill chains can be generated when communication is constrained. Finally, practical examples show the reliability and effectiveness of the proposed algorithm.

The optimization of equipment system combination is crucial for the pre-deployment decision-making of our military. In order to obtain advantageous equipment portfolio solutions, we establish a multi-layer network optimization model for equipment systems and introduce a novel multi-objective optimization algorithm for equipment portfolios in combat systems. Firstly, a multi-objective optimization model for equipment system combination recommendation is established, with the objective of optimizing combat network redundancy and vulnerability. Secondly, due to the complexity of the equipment system optimization model and the challenge of finding optimal equipment portfolios, we propose a clustering-assisted preselection multi-objective evolutionary algorithm called MECAP. This algorithm is designed to enhance the convergence of the population towards the optimal solution set. Additionally, the experimental results highlight the benefits of applying MECAP for offspring generation strategy after model sampling.