Extended Signal Processing by Sparse Arrays and Its Application to High-Resolution Sensing

2022 Fiscal Year Final Research Report

• Project/Area Number: 20K04500
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 21030:Measurement engineering-related
• Research Institution: Yokohama National University
• Principal Investigator: Ichige Koichi 横浜国立大学, 大学院工学研究院, 教授 (10313470)
• Project Period (FY): 2020-04-01 – 2023-03-31
• Keywords: スパースアレー / センシング / 拡張アレー信号処理

Outline of Final Research Achievements

This research aims at developing high resolution sensing using sparse arrays and extended signal processing. The achievements are (1) high efficiency sparse linear arrays, (2) optimization of 2-D sparse arrays based on deep learning, and (3) beamforming application and signal modulation/demodulation. In (1) sparse linear arrays, a novel sparse array configuration called GENAMS (Generalized ENAMS) has been proposed which enhance degree of freedom while suppressing mutual coupling effect. So far it is regarded as one of the best sparse linear arrays.

Free Research Field

計測工学

Academic Significance and Societal Importance of the Research Achievements

新たなスパースリニアアレー構造を構築したこと，およびそのアレー構造がアレー自由度を大いに高めつつ，相互結合の影響を低減したこと，さらにサブアレー化により計算コスト低減の目途をつけたことが挙げられる．特にGENAMS (Generalized ENAMS)は，現時点では世界で最も優れたスパースリニアアレー構造として認識されており，今後さらなる発展が期待できる．