L1/2-norm-based sparse linear array optimization method

Abstract

The invention relates to an L1 / 2-norm-based sparse linear array optimization method. The method is characterized by comprising the basic steps of determining an initial array and a weighting matrix, determining array weight vectors, judging whether the stimulus of head and tail array elements in the array weight vector is greater than a set stimulus minimum delta or not, judging whether an L1 norm for optimizing a difference between the previous and latter array weight vectors is smaller than a set error minimum xi or not, and determining the array element positions and stimulus of the sparse linear array. According to the method, the non-convex optimization problem of solution to an L1 / 2 norm is converted into a series of convex optimization problems of the L1 norm, so that a sparse array with lower sparsity can be obtained to reduce the number of actually required array elements on the premise of substantially keeping calculation unchanged; meanwhile, the head and tail array elements of the array are constrained and adaptively regulated under the condition of given array aperture, so that the problem of deficiency of the head and tail array elements of the sparse array in an iterative convex optimization process is well solved, and the method is particularly applied to the place of optimization of a large-sized antenna array.

Description

technical field [0001] The invention belongs to the technical field of antenna array optimization, in particular to an L-based 1 / 2 A sparse linear array optimization method for norm. Background technique [0002] In radar, communication, sonar, ultrasonic imaging and other electronic systems, in order to make the antenna beam have strong directivity, low sidelobe, easy scanning and other performance indicators, antenna arrays have been widely used, and the optimal design of antenna arrays has become a modern electronic system. A very important link in the design. In the early research of antenna arrays, evenly spaced arrays have been extensively studied due to their simple design, convenient mathematical processing, and ease of implementation. However, it has two major disadvantages: first, in order to avoid the appearance of grating lobes, the distance between array elements is usually not greater than half of the wavelength. If the wavelength is small, the array will be ...

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Problems solved by technology

However, it has two major disadvantages: first, in order to avoid the appearance of grating lobes, the distance between array elements is usually not greater than half of the wavelength. If the wavelength is small, the array will be too dense and cause serious mutual coupling between array elements; If the array has a higher resolution, the aperture of the array will be very large, and more array elements will be required for a uniform array, which will significantly increase the cost and cost of the system

However, given the length of the array aperture, since the above two methods do not constrain the head and tail elements of the optimized array, the constraints of the array aperture will not be met during the optimization process due to the absence of the head and tail elements of the sparse array

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