Large-scale phased-array antenna sub array division method based on weighted K average value clustering

Abstract

The invention discloses a large-scale phased-array antenna sub array division method based on weighted K average value clustering. When the method is used, under the condition of nonuniform array element stage weighting, the smaller weight vector approximate error is obtained, so that the static different wave beams with better performance can be integrated. According to the method, firstly, a subarray single pulse signal processing module is built; then, under the weight vector approximate criterion, the subarray optimization sum difference wave beam integration problem is converted into the reference sum different weight ratio clustering problem; the relationship between the weight vector approximate error and the clustering error is analyzed; finally, under the nonuniform array element stage weight condition, a weighted K average value clustering algorithm is adopted for optimizing the subarray structure; a least square method is used for optimizing the subarray weighing. Under the nonuniform array element stage weighting condition, the method provided by the invention can obtain smaller weight vector approximate error than a traditional clustering algorithm, and the static difference wave beam with better performance can be integrated.

Description

technical field [0001] The invention relates to the technical field of large phased array radars, in particular to a method for dividing large phased array antenna sub-arrays based on weighted K-means clustering. Background technique [0002] In a large-scale phased array radar array antenna, there are generally hundreds or even tens of thousands of antenna elements. If fully adaptive digital beamforming is to be realized, the received signal of each array element needs to be processed separately, so that the system will change It is very complicated and the hardware cost is very high. Moreover, the dimension and complexity of the adaptive algorithm are very large, and it is difficult to meet the real-time requirements. For this situation, it is often necessary to divide the large array into sub-arrays. Sub-array division is to combine all array elements into several small-scale sub-arrays, and then complete part of the adaptive processing at the sub-array level, thereby r...

AI Technical Summary

Problems solved by technology

When the element-level weighting is not uniform (such as low sidelobe Taylor weighting), the weight vector approximation error is no longer equal to the clustering error, and the traditional clustering algorithm cannot completely minimize the approximation error, which affects the pattern of the difference beam performance

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