Secondary Optimal Design of Radar Unit Based on Hierarchical Gaussian Mixture Model

The phased array radar radiation unit is a precision product in the military industry. In recent years, with the advancement of automation technology, the radar manufacturing technology has been greatly developed, but the design is relatively lagging. After investigation, it is found that a lot of data information is generated during the production of the radar, which can be used to guide the design of the radar. At present, this information is not effectively used. For this reason, this paper proposes a radar secondary design optimization algorithm based on the hierarchical Gaussian mixture model, which aims to use historical production data to perform secondary optimization on the radar design and realize the closed loop of design and manufacturing. The experimental results show that the quality of radar production has been greatly improved after the design optimization of the method in this paper, which verifies the effectiveness of the algorithm.