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Sparse Array Synthesis Based on Multi-beam Joint Convex Optimization Algorithm | IEEE Conference Publication | IEEE Xplore

Sparse Array Synthesis Based on Multi-beam Joint Convex Optimization Algorithm


Abstract:

A multi-beam joint convex optimization algorithm based on the compressed sensing theory is presented for simultaneous-multi-beam synthesis. It is based on the directional...Show More

Abstract:

A multi-beam joint convex optimization algorithm based on the compressed sensing theory is presented for simultaneous-multi-beam synthesis. It is based on the directional pattern envelope and beam scanning characteristics of a uniform array. In this algorithm the element number, position and excitation of uniform array are optimized for the desired directional pattern envelope of certain-angle-range-different beams. The number and position of the sparse array elements under each beam pointing are then fixed within a beam scanning range for multiple envelopes of different expected patterns. This algorithm optimizes the array element excitation with the convex optimization method. Numerical results show the high precision and efficiency of this method to achieve multiple desired radiation patterns with a sparse non-uniform linear array.
Date of Conference: 13-15 November 2021
Date Added to IEEE Xplore: 10 January 2022
ISBN Information:
Conference Location: Chongqing, China

Funding Agency:


I. Introduction

With the modern complex electromagnetic environment and the increasing requirements for radar detection, the development of large antenna arrays increases the complexity, cost and power consumption of antenna systems. The antenna array is often sparse and optimally arrayed to satisfy the desired array pattern with the minimum number of elements to reduce the weight and cost of the radar system. Many optimization algorithms have been proposed in the last sixty years to synthesize such arrays [1]–[5], such as follows: analytical methods, intelligent optimization methods, fast Fourier transform methods, matrix beam methods, compressed sensing methods, etc. Despite the success of these methods, most of them can only be used to synthesize a sparse array with a single-pattern. They will be invalid in the multiple-pattern case since the best element positions usually change with different patterns. Only a few techniques have been proposed for the synthesis of a non-uniform sparse array with multiple-pattern[6]–[9]. Combined with the theory of compressed sensing, this paper is aimed at proposing a new method to synthesize a multiple-pattern array with as few elements as possible.

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References

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