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HiRIS: an Airborne Sonar Sensor with a 1024 Channel Microphone Array for In-Air Acoustic Imaging

Dennis Laurijssen, Walter Daems, Jan Steckel

TL;DR

This work addresses the need for high-resolution in-air ultrasound imaging by introducing HiRIS, a 1024-channel microphone array implemented as a dense 32×32 grid. It combines a distributed ARM-based hardware architecture with MVDR beamforming and Forward-Backward Spatial Smoothing to produce accurate 2D and 3D acoustic images, validated through simulated PSFs and real passive and active measurements. Key contributions include the detailed hardware architecture, a scalable data acquisition chain, and a processing pipeline that yields up to a 70 dB main lobe to sidelobe ratio, enabling virtually artifact-free imaging. The approach advances airborne ultrasound sensing toward a practical upper limit of performance, with potential for open datasets and broader robotic applications in harsh environments.

Abstract

Airborne 3D imaging using ultrasound is a promising sensing modality for robotic applications in harsh environments. Over the last decade, several high-performance systems have been proposed in the literature. Most of these sensors use a reduced aperture microphone array, leading to artifacts in the resulting acoustic images. This paper presents a novel in-air ultrasound sensor that incorporates 1024 microphones, in a 32-by- 32 uniform rectangular array, in combination with a distributed embedded hardware design to perform the data acquisition. Using a broadband Minimum Variance Distortionless Response (MVDR) beamformer with Forward-Backward Spatial Smoothing (FB-SS), the sensor is able to create both 2D and 3D ultrasound images of the full-frontal hemisphere with high angular accuracy with up to 70dB main lobe to side lobe ratio. This paper describes both the hardware infrastructure needed to obtain such highly detailed acoustical images, as well as the signal processing chain needed to convert the raw acoustic data into said images. Utilizing this novel high-resolution ultrasound imaging sensor, we wish to investigate the limits of both passive and active airborne ultrasound sensing by utilizing this virtually artifact-free imaging modality.

HiRIS: an Airborne Sonar Sensor with a 1024 Channel Microphone Array for In-Air Acoustic Imaging

TL;DR

This work addresses the need for high-resolution in-air ultrasound imaging by introducing HiRIS, a 1024-channel microphone array implemented as a dense 32×32 grid. It combines a distributed ARM-based hardware architecture with MVDR beamforming and Forward-Backward Spatial Smoothing to produce accurate 2D and 3D acoustic images, validated through simulated PSFs and real passive and active measurements. Key contributions include the detailed hardware architecture, a scalable data acquisition chain, and a processing pipeline that yields up to a 70 dB main lobe to sidelobe ratio, enabling virtually artifact-free imaging. The approach advances airborne ultrasound sensing toward a practical upper limit of performance, with potential for open datasets and broader robotic applications in harsh environments.

Abstract

Airborne 3D imaging using ultrasound is a promising sensing modality for robotic applications in harsh environments. Over the last decade, several high-performance systems have been proposed in the literature. Most of these sensors use a reduced aperture microphone array, leading to artifacts in the resulting acoustic images. This paper presents a novel in-air ultrasound sensor that incorporates 1024 microphones, in a 32-by- 32 uniform rectangular array, in combination with a distributed embedded hardware design to perform the data acquisition. Using a broadband Minimum Variance Distortionless Response (MVDR) beamformer with Forward-Backward Spatial Smoothing (FB-SS), the sensor is able to create both 2D and 3D ultrasound images of the full-frontal hemisphere with high angular accuracy with up to 70dB main lobe to side lobe ratio. This paper describes both the hardware infrastructure needed to obtain such highly detailed acoustical images, as well as the signal processing chain needed to convert the raw acoustic data into said images. Utilizing this novel high-resolution ultrasound imaging sensor, we wish to investigate the limits of both passive and active airborne ultrasound sensing by utilizing this virtually artifact-free imaging modality.
Paper Structure (16 sections, 5 equations, 5 figures)

This paper contains 16 sections, 5 equations, 5 figures.

Table of Contents

  1. Introduction
  2. Hardware Architecture
  3. Design Choices
  4. Distributed Architecture
  5. Front end
  6. Back end
  7. Physical Realisation of the HiRIS Sensor
  8. Data acquisition and Processing Chain
  9. DAQ
  10. Image Formation using MVDR
  11. Verification of HiRIS
  12. Simulation of Point Spread Functions
  13. Real-world validation: Setup
  14. Real-world validation: Passive measurement
  15. Active Measurements
  16. ...and 1 more sections

Figures (5)

  • Figure 1: Overview of the HiRIS hardware architecture. Panel a) show a schematic representation of the system architecture, distinguishing between the front-end and back-end PCBs. On the front-end, there are 32 groups of 32 microphones (each arranged in an 8x4 grid). The back-end PCB has one primary node which does clock distribution, triggering and synchronization of the subordinate nodes. The subordinate nodes each sample a 32-channel microphone group, using 16 IO pins with the microphones operating in dual-channel stereo mode. Panel b) shows the assembled front-end PCB measuring 180mm by 170mm. Box e) indicates a single 8x4 group of microphones. Boxes labeled d) indicate the interconnect connectors between the front-end and back-end PCBs. Panel c) shows the assembled back-end PCB, which has the same dimensions as the front-end PCB. Panel e) indicates the primary node, and box f) shows a single subordinate node.
  • Figure 2: Coordinate system in relationship to the HiRIS sensor, showing the X, Y and Z axis, and the azimuth angle $\theta$ and elevation angle $\phi$.
  • Figure 3: Point Spread Functions of point sources placed at different spatial locations: panel a & d) $(\theta,\phi)=(0^{\circ},0^{\circ})$, panel b & e) $(\theta,\phi) = (30^{\circ},0^{\circ})$, and panel c & f) $(\theta,\phi)=(-45^{\circ},45^{\circ})$. Panels a-c) show the response of the system using Bartlett beamforming, and panels d-f) show the response when using the MVDR beamformer. The PSFs are shown on a logarithmic scale.
  • Figure 4: The realized prototype of HiRIS. Panel a) shows the front-view of the sensor with the microphone port-holes, and the 33 USB cables used to connect the nodes to the USB hubs. Panel b) shows the backside of the back-end PCB, with the USB cables connecting all the nodes to the USB hubs,and shows the copper cooling solution provisioned for heat management. The four USB hubs are then connected to an aggregate USB hub, which is connected to the host computer. Panel c) shows the front-view of the HiRIS sensor, where the component-less front-side is visible, with the exception of the holes for the bottom-mounted MEMS microphones. Panel d) shows the cluttered office space which has been ensonified during the active measurement experiment.
  • Figure 5: Experimental results of the HiRIS sensor. Panels a-c show the response of a 40-kHz source placed in front of the HiRIS sensor, using various processing techniques (a: Delay and Sum, b: MVDR and c: Bartlett beamforming), on a logarithmic scale. Panels b-f) show the response on a linear scale. Panel g) shows the B-mode image of a scene ensonified using a broadband chirp, and processed using the algorithm described in this paper.