Passive source localization based on time difference of arrival (TDOA) is an important method for high-precision and fast localization of point sources in areas such as gunshot localization and remote missile landing point localization. Traditional multi-element array passive sound localization algorithms have nonlinear equation, and their analytical solutions are obtained through complex transformations, which may result in non-unique solutions and low localization accuracy, making them unsuitable for practical applications. In this paper, a five-element tetrahedral array localization model is proposed, which utilizes the redundant degrees of freedom contained in the five-element array and introduces an intermediate variable (redundant variable) to linearize the resulting equation system. The accuracy of the analytical solution obtained by this optimization algorithm and the directional and distance errors under this array model are analyzed.
KEYWORDS: Point clouds, Detection and tracking algorithms, Data modeling, Target recognition, 3D modeling, Matrices, Mathematical optimization, Tunable filters, Reliability, Clouds
A point cloud matching algorithm based on point pair features with improved ICP is proposed to address the problems of slow target recognition and low accuracy in complex scenes such as occlusion and overlap. In the offline training phase, the algorithm creates a global model description based on directed point pair features and uses hash table storage to speed up the finding time; in the online matching phase, the generalized Hoff voting strategy is used to complete the point pair matching and generate candidate poses; finally, the obtained candidate poses are clustered and filtered and the poses are optimized by the overlap rate between the field point cloud and the model point cloud and the improved ICP algorithm. In this paper, the proposed algorithm is experimented for different complex scenes, and the experimental results prove that the recognition rate and feature point matching rate of this paper are high and have good robustness for the point cloud data with large data volume and poor quality.
By applying the dynamic Bayesian network theory, a dynamic Bayesian network evaluation model is established according to the structure, damage level and functional characteristics of military airport buildings. Based on the construction of the airport model, a Bayesian network diagram for evaluating the damage effect of military airport buildings is established by using the powerful inference function of dynamic Bayesian network. On the basis of determining the damage evaluation network nodes of military airport, the network structure and network parameters are determined. The simulation experiment verifies the correctness and effectiveness of the calculation. This model can not only evaluate the damage of the whole airport building, but also evaluate the damage effect of the airport building structure and other facilities. It provides scientific assessment for the decision makers' flexible strike methods and strike tasks.
KEYWORDS: Modeling, Visualization, OpenGL, 3D modeling, 3D applications, Visual process modeling, Technology, Systems modeling, Coastal modeling, Displays
A method of building hull model with OpenGL and C++ is proposed, which can better realize 3D modeling. Compared with the traditional 2D method, 3D design can express products more intuitively. Compared with some major commercial modeling software on the market, although commercial software can establish a complete surface space, the entire system modeling cannot be separated from the operation of commercial software, and software application cannot be promoted without independent intellectual property rights. The model constructed by this method analyzes the complex curves and surfaces contained in the shape of the hull according to the characteristics of the hull, and proposes a geometric modeling algorithm based on key points, which uses quadrilateral patches to splicing key points. Implement model meshing. Finally, a parametric model is formed by OpenGL rendering. The hull parametric model system developed based on OpenGL has good stability and strong compatibility, and can better assist in the evaluation of ship damage effects and target vulnerability, which are two extremely important tasks.
According to the problem of transmitter and receiver node arrangement of multistatic buoy in underwater area surveillance, this paper presents an array optimization method based on GAPSO (Genetic Algorithm - Particle Swarm Optimization). Firstly, the performance evaluation model of multistatic buoy array is established by probability fusion based on the bistatic sonars equation. We take the effective coverage rate of the model as the objective function, then Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are used to optimize the array. Secondly, PSO and GA are combined to improve the global searching ability and convergence speed. The simulation results show that the coverage ability of the optimized multistatic buoy array is significantly improved compared with the traditional array scheme. Compared with PSO and GA, fused GAPSO has obvious improvement in search ability and convergence speed, which achieves the purpose of optimal defense deployment.
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