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A tensor model for the calibration of air-coupled ultrasonic sensor arrays in 3D imaging

Müller, Raphael ; Allevato, Gianni ; Rutsch, Matthias ; Haugwitz, Christoph ; Liu, Tianyi ; Kupnik, Mario ; Pesavento, Marius (2025)
A tensor model for the calibration of air-coupled ultrasonic sensor arrays in 3D imaging.
In: Signal Processing, 230
doi: 10.1016/j.sigpro.2024.109812
Artikel, Bibliographie

Kurzbeschreibung (Abstract)

Arrays of ultrasonic sensors are capable of 3D imaging in air and an affordable supplement to other sensing modalities, such as radar, lidar, and camera, i.e.in heterogeneous sensing systems. However, manufacturing tolerances of air-coupled ultrasonic sensors may lead to amplitude and phase deviations. Together with artifacts from imperfect knowledge of the array geometry, there are numerous factors that can impair the imaging performance of an array. We propose a reference-based calibration method to overcome possible limitations. First, we introduce a novel tensor signal model to capture the characteristics of piezoelectric ultrasonic transducers (PUTs) and the underlying multidimensional nature of a multiple-input multiple-output (MIMO) sensor array. Second, we formulate and solve an optimization problem based on this model to obtain the calibrated parameters of the array. Third, we assess both our model and the commonly used analytic model using real data from a 3D imaging experiment. The experiment reveals that our array response model we learned with calibration data yields an imaging performance similar to that of the analytic array model, which requires perfect array geometry information.

Typ des Eintrags: Artikel
Erschienen: 2025
Autor(en): Müller, Raphael ; Allevato, Gianni ; Rutsch, Matthias ; Haugwitz, Christoph ; Liu, Tianyi ; Kupnik, Mario ; Pesavento, Marius
Art des Eintrags: Bibliographie
Titel: A tensor model for the calibration of air-coupled ultrasonic sensor arrays in 3D imaging
Sprache: Englisch
Publikationsjahr: Mai 2025
Verlag: Elsevier
Titel der Zeitschrift, Zeitung oder Schriftenreihe: Signal Processing
Jahrgang/Volume einer Zeitschrift: 230
DOI: 10.1016/j.sigpro.2024.109812
URL / URN: https://www.sciencedirect.com/science/article/pii/S016516842...
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Kurzbeschreibung (Abstract):

Arrays of ultrasonic sensors are capable of 3D imaging in air and an affordable supplement to other sensing modalities, such as radar, lidar, and camera, i.e.in heterogeneous sensing systems. However, manufacturing tolerances of air-coupled ultrasonic sensors may lead to amplitude and phase deviations. Together with artifacts from imperfect knowledge of the array geometry, there are numerous factors that can impair the imaging performance of an array. We propose a reference-based calibration method to overcome possible limitations. First, we introduce a novel tensor signal model to capture the characteristics of piezoelectric ultrasonic transducers (PUTs) and the underlying multidimensional nature of a multiple-input multiple-output (MIMO) sensor array. Second, we formulate and solve an optimization problem based on this model to obtain the calibrated parameters of the array. Third, we assess both our model and the commonly used analytic model using real data from a 3D imaging experiment. The experiment reveals that our array response model we learned with calibration data yields an imaging performance similar to that of the analytic array model, which requires perfect array geometry information.

Freie Schlagworte: airborne ultrasound, array response modeling, dictionary learning, low-rank tensor approximation, MIMO imaging, sparse representation
ID-Nummer: Artikel-ID: 109812
Fachbereich(e)/-gebiet(e): 18 Fachbereich Elektrotechnik und Informationstechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Mess- und Sensortechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Nachrichtentechnische Systeme
Hinterlegungsdatum: 16 Jan 2025 13:03
Letzte Änderung: 16 Jan 2025 13:03
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