This paper proposes a track association algorithm based on convolutional neural network. The algorithm uses multi-time history correlation local track estimation, and adjusts the position coordinate estimation in the track data to classify the track association. It is used to solve the technical difficulties of track splitting and redundancy caused by track association of multiple radar stations. After simulation analysis, the results show that the intelligent track association algorithm proposed in this paper has higher correct association probability and better universality.
With the development of computer vision, visual servo control has been widely used in various fields. This article analyzed the stability of the image visual servo system in application, and provided theoretical and practical data support to engineering researchers when using the technology. The Jacobian matrix of the image based on point features was deduced in detail, and a simulation model in an ideal environment was built to show the convergence process. A 6-DOF manipulator was used for practical application experiments, and several measures to improve stability were described for the existing divergence problems, and the reliability of the technology in engineering applications was improved.
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