Quick arbitrarily shaped directional pattern major lobe maintenance self-adaptive beam-forming method

Abstract

The invention relates to a quick arbitrarily shaped directional pattern major lobe maintenance self-adaptive beam-forming method. The method comprises the steps that a principal eigenvector of a major lobe covariance matrix is selected to construct a major lobe area gain and linear constraint matrix, meanwhile, a constraint response vector corresponding to the major lobe area gain and linear constraint matrix is determined, and a linear constraint optimal beam shaper model containing weighting vector quadratic constraints is constructed; quadratic constraint requirements for weighting vectors are relaxed, and a closed-form optimal weighting vector expression of a diagonal loading covariance matrix structure under a GSC architecture is obtained; an optimal diagonal loading capacity meeting quadratic constraint conditions is estimated through an iterative equation, the optimal diagonal loading capacity is substituted into the closed-form self-adaptive optimal weighting vector expression, and a weighting vector corresponding to a major lobe maintenance self-adaptive interference suppression beam is obtained. Quick self-adaptive anti-interference under the major lobe maintenance constraint of an arbitrarily shaped antenna directional pattern is achieved under the GSC architecture, and while the directional pattern major lobe maintenance performance is taken into consideration, the computation complexity is effectively reduced.

Description

Technical field [0001] The invention belongs to the field of array antenna space-domain adaptive anti-jamming, and specifically relates to a fast and arbitrary shape pattern main lobe shape-preserving adaptive beam forming method. [0002] technical background [0003] The current arbitrary shape pattern synthesis algorithms for digital array antennas, including arbitrary shape static pattern synthesis algorithms and arbitrary shape pattern synthesis algorithms with nulls, are built on the mean square error between the synthesized pattern and the desired pattern. The pattern approximation optimization under the minimum criterion, when a null pattern needs to be formed to achieve interference airspace suppression, the arrival angle of all interference must be estimated first. [0004] However, the adaptive digital beamforming algorithm does not require prior information such as interference angle, and can adaptively calculate weight coefficients based on real-time sampling data, gener...

AI Technical Summary

Problems solved by technology

These robust algorithms have the ability to preserve the shape of the pattern, such as the diagonally loaded beamformer. The main problem is that the selection of the loading amount is also difficult.

In addition, basically all current robust beamforming algorithms are optimized for spot beams or sum-difference beams. The main lobe shape-preserving problem of any static pattern with wide-angle coverage is still the adaptive beamforming interference under the main-lobe shape-preserving condition. The main difficulty of suppression technology

[0006] To solve the problem of wide beam coverage, the steering vectors in several directions in the main lobe can be uniformly selected to form a uniform constraint matrix to control the gain of the main lobe. However, for beams with wider beam widths, this method is difficult to control the The gain is fluctuating, and it is difficult to select the direction that needs to be constrained in the main lobe, so the determination of the main lobe constraint is particularly important for the main lobe shape retention

Images