Pattern Synthesis for Sparse Arrays by Compressed Sensing and Low-Rank Matrix Recovery Methods

Ting Wang,Ke-Wen Xia,Ning Lu

doi:10.1155/2018/6403269

Abstract

Antenna array pattern synthesis technology plays a vital role in the field of smart antennas. It is well known that the pattern synthesis of homogeneous array is the key topic of pattern synthesis technology. But this technology needs plenty of homogeneous array elements to meet the antenna requirements. So, a novel pattern synthesis technology for sparse array based on the compressed sensing (CS) and low-rank matrix recovery (LRMR) methods is proposed. The proposed technology predominantly includes the design of sparse array, the recovery of homogeneous array, and the synthesis of antenna array pattern. The simulation result shows that an antenna array with low gain and strong directivity can be arbitrarily built by the use of a small amount of sparse array elements and it is useful for the miniaturization and economical efficiency of the antenna system.

Highlights

Antenna is the device to radiate and receive radio waves
As a result of this application, we can get the effect of pattern synthesis of the homogeneous array with sparse array and it is useful for the miniaturization and economical efficiency of the antenna system
To the best our knowledge, this is the first work to incorporate both compressed sensing and low-rank matrix recovery methods for pattern synthesis to obtain the results with less sparse elements which is the main requirement for smart antennas

Summary

Introduction

Antenna is the device to radiate and receive radio waves. The array antenna has the characteristics of strong directivity and high gain, which can satisfy radiation character and is appropriate in engineering application. The optimization algorithms are used to restore the homogeneous array from the sparse array and the result of pattern synthesis is obtained later. Wang et al [6] put forward several kinds of uniform linear array low side lobe pattern synthesis algorithms. The rise of compressed sensing (CS) [8] and low-rank matrix recovery (LRMR) [9] theories in recent years provides a new solution These developments contribute to making sparse representation a more effective and popular way for data representation. As a result of this application, we can get the effect of pattern synthesis of the homogeneous array with sparse array and it is useful for the miniaturization and economical efficiency of the antenna system.

Related Works

Problem Formulation

Proposed Method

Experiments and Simulation Results

Conclusion