Multidimensional compressive sensing based technologies for next-generation MIMO radar with SL3: Super-resolution, Low-complexity, Low-cost and Low-consumption

2019 Fiscal Year Final Research Report

• Project/Area Number: 15K06072
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: Communication/Network engineering
• Research Institution: Akita Prefectural University
• Principal Investigator: Xu Li 秋田県立大学, システム科学技術学部, 教授 (40252324)
• Co-Investigator(Kenkyū-buntansha): 桂 冠 秋田県立大学, システム科学技術学部, 特任助教 (80734904) ; 松下 慎也 秋田県立大学, システム科学技術学部, 准教授 (20435449)
• Project Period (FY): 2015-04-01 – 2020-03-31
• Keywords: compressive sensing / sparse representation / sparse signal processing / adaptive algorithm / MIMO radar system / communication system / DOA estimation

Outline of Final Research Achievements

The compressive sensing (CS) based signal processing for MIMO radar can achieve a high resolution even using a sub-Nyquist sampling rate. However, the existing CS-based methods usually suffer a high computational complexity for signal reconstruction and a low accuracy for non-Gaussian impulsive noise environment. This research has developed a class of new CS-based methods with higher resolution, less complexity and stronger robustness against non-Gaussian impulsive noise than the existing CS-based methods. Specifically, robust 2D-CS-based methods for the range and velocity joint detection problem have been established, which can reduce the computational complexity largely and significantly improve the performance for non-Gaussian impulsive noise environment. Moreover, a novel two-stage method has been proposed for the DOA estimation: first identify certain candidate areas with a relatively low-resolution, then locate the targets with a high-resolution in the obtained candidate areas.

Free Research Field

System Control Engineering

Academic Significance and Societal Importance of the Research Achievements

本研究の成果は次世代MIMOレーダーの信号処理における基本課題に対し新しい知見と解法を与えるものであり，レーダー工学と情報処理工学の発展に寄与することが期待できる．特に，2次元圧縮センシングに基づくアルゴリズムは，低速のADCや少ない反射信号のスナップショットを用いても，効率的かつ高精度の目標探知ができるため，短期天気予報や自動運転など高精度およびリアルタイムの信号処理が求められるシステムに応用することが可能である．