{"title":"用于压缩三维空间传感的多频单通道空气耦合 PMUT 的理论验证","authors":"Tingzhong Xu;Zhongjie Zhang;Rodrigo Tumolin Rocha;Liang Zeng;Chunlei Xu","doi":"10.1109/OJUFFC.2024.3408138","DOIUrl":null,"url":null,"abstract":"This paper proposes a new 3D spatial sensing approach via compressed sensing (CS) by using a single-channel air-coupled piezoelectric micromachined ultrasonic transducer (PMUT) operated with multi-frequency. Our study focuses on a single-channel transducer with a PMUT array composed of several diaphragms with different radius sizes. It is known that small variations in the radius size can cause distinct transmission signals of all diaphragms that are excited by the same excitation signal. In this way, the acoustic field distribution of a region of interest (ROI) can be distorted especially in the direction perpendicular to the wave propagation, which could help to obtain more distinctive information about the scatterers at different locations in any 3D ROI. Therefore, a compressed 3D spatial sensing approach is proposed and used for acquiring measurements of the designed single-channel transducer. The information of any object in a 3D ROI can be mapped onto a collection of basis functions constructed via the nearly mutual orthogonal echo signals from all scatterers in the ROI. Furthermore, the proposed approach is verified with simulated acoustic measurements obtained from the established PMUT equivalent circuit model and the K-Wave acoustic propagation model via an obstacle-sensing application. Based on the sparsity nature of objects in the ROI, the reconstruction of 2D/3D images of objects can be accomplished via a CS-based algorithm. The obtained image reconstruction results show that the proposed approach allows not only for detecting localization but also for reconstructing descriptive features of an object.","PeriodicalId":73301,"journal":{"name":"IEEE open journal of ultrasonics, ferroelectrics, and frequency control","volume":"4 ","pages":"37-51"},"PeriodicalIF":0.0000,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10545345","citationCount":"0","resultStr":"{\"title\":\"Theoretical Validation of a Single-Channel Air-Coupled PMUT With Multi-Frequency Operation for Compressed 3D Spatial Sensing\",\"authors\":\"Tingzhong Xu;Zhongjie Zhang;Rodrigo Tumolin Rocha;Liang Zeng;Chunlei Xu\",\"doi\":\"10.1109/OJUFFC.2024.3408138\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a new 3D spatial sensing approach via compressed sensing (CS) by using a single-channel air-coupled piezoelectric micromachined ultrasonic transducer (PMUT) operated with multi-frequency. Our study focuses on a single-channel transducer with a PMUT array composed of several diaphragms with different radius sizes. It is known that small variations in the radius size can cause distinct transmission signals of all diaphragms that are excited by the same excitation signal. In this way, the acoustic field distribution of a region of interest (ROI) can be distorted especially in the direction perpendicular to the wave propagation, which could help to obtain more distinctive information about the scatterers at different locations in any 3D ROI. Therefore, a compressed 3D spatial sensing approach is proposed and used for acquiring measurements of the designed single-channel transducer. The information of any object in a 3D ROI can be mapped onto a collection of basis functions constructed via the nearly mutual orthogonal echo signals from all scatterers in the ROI. Furthermore, the proposed approach is verified with simulated acoustic measurements obtained from the established PMUT equivalent circuit model and the K-Wave acoustic propagation model via an obstacle-sensing application. Based on the sparsity nature of objects in the ROI, the reconstruction of 2D/3D images of objects can be accomplished via a CS-based algorithm. 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引用次数: 0
摘要
本文提出了一种新的三维空间传感方法,即使用单通道空气耦合压电微机械超声换能器(PMUT)进行多频压缩传感(CS)。我们的研究侧重于单通道换能器,其 PMUT 阵列由多个半径不同的膜片组成。众所周知,半径大小的微小变化会导致被同一激励信号激发的所有膜片产生不同的传输信号。这样,感兴趣区域(ROI)的声场分布就会失真,特别是在垂直于波传播的方向上,这有助于获得有关任何三维 ROI 中不同位置散射体的更多独特信息。因此,我们提出了一种压缩三维空间传感方法,并将其用于获取所设计的单通道传感器的测量结果。三维 ROI 中任何物体的信息都可以映射到通过来自 ROI 中所有散射体的几乎相互正交的回波信号构建的基函数集合上。此外,通过障碍物感应应用,利用已建立的 PMUT 等效电路模型和 K 波声传播模型获得的模拟声学测量结果,对所提出的方法进行了验证。基于 ROI 中物体的稀疏性,可以通过基于 CS 的算法重建物体的 2D/3D 图像。获得的图像重建结果表明,所提出的方法不仅能检测定位,还能重建物体的描述性特征。
Theoretical Validation of a Single-Channel Air-Coupled PMUT With Multi-Frequency Operation for Compressed 3D Spatial Sensing
This paper proposes a new 3D spatial sensing approach via compressed sensing (CS) by using a single-channel air-coupled piezoelectric micromachined ultrasonic transducer (PMUT) operated with multi-frequency. Our study focuses on a single-channel transducer with a PMUT array composed of several diaphragms with different radius sizes. It is known that small variations in the radius size can cause distinct transmission signals of all diaphragms that are excited by the same excitation signal. In this way, the acoustic field distribution of a region of interest (ROI) can be distorted especially in the direction perpendicular to the wave propagation, which could help to obtain more distinctive information about the scatterers at different locations in any 3D ROI. Therefore, a compressed 3D spatial sensing approach is proposed and used for acquiring measurements of the designed single-channel transducer. The information of any object in a 3D ROI can be mapped onto a collection of basis functions constructed via the nearly mutual orthogonal echo signals from all scatterers in the ROI. Furthermore, the proposed approach is verified with simulated acoustic measurements obtained from the established PMUT equivalent circuit model and the K-Wave acoustic propagation model via an obstacle-sensing application. Based on the sparsity nature of objects in the ROI, the reconstruction of 2D/3D images of objects can be accomplished via a CS-based algorithm. The obtained image reconstruction results show that the proposed approach allows not only for detecting localization but also for reconstructing descriptive features of an object.