Distributed low-cost microphone array for 3D localization for bio-acoustic applications

Verreycken, Erik; Daems, Walter; Steckel, Jan

doi:10.1109/icsens.2017.8234059

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…While the three aforementioned types of embedded devices, being FPGAs [3], [4], [10]- [12], SoCs [13]- [15] and ARM microcontrollers [5], [16]- [19], have been used for the construction of high-resolution ultrasound sensing arrays, each of these device types have their distinct advantages and disadvantages.…”

Section: A Design Choicesmentioning

confidence: 99%

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

Laurijssen,

Daems,

Steckel

2024

IEEE Access

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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 ratioin the array response in a single-source scenario. 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 highresolution 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.

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Section: A Design Choicesmentioning

confidence: 99%

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

Laurijssen,

Daems,

Steckel

2024

IEEE Access

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“…This test signal is coupled to a combination of a custom PCB with buffer amplifiers, sampling filters, an ADS8556 ADC [ 18 ], and the direct IO inputs of the Beaglebone Black’s coprocessor [ 19 ]. This combination was manually developed by Verreycken et al [ 20 ] for classical Nyquist sampling. The firmware for this project was manually developed in approximately 15 working days by an engineer skilled for the job.…”

Section: Generated Firmware In Actionmentioning

confidence: 99%

Automated Firmware Generation for Compressive Sensing on Heterogeneous Hardware

Baeyens

Denil

Steckel

et al. 2022

Sensors

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In this paper, a model-based firmware generator is presented towards complex sampling schemes. The framework is capable of automatically generating a fixed-rate Shannon-compliant acquisition scheme, as well as a variable-rate compressive sensing acquisition scheme. The generation starts from a model definition, which consists of two main components, namely an acquisition sequence to implement and the platform on which the sequence should be implemented. This model is then combined with the specifications to be transformed into a functional firmware. When generating firmware for compressive sensing (CS) purposes, the defined acquisition sequence is automatically generated to implement a pseudo-random sampling scheme in agreement with the defined undersampling factor. The evaluation of the generated firmware is done by means of an example use-case, including a proposed strategy for synchronization between CS setups. This research attempts to reduce the development complexity for embedded CS to lower the threshold towards effective usage in the field.

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“…Figure 1 presents the necessary components for a signal processing system. In these types of applications, sensor data originating from various sensing sources need to be digitized at high speeds of sometimes up to the MHz range [1][2][3][4][5][6], with 8-to 32-bit quantization and on multiple channels simultaneously. In these cases, robust and stable sampling is crucial for the validity and performance of the subsequent signal processing steps.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Firmware Generation for Acquisition Systems Using Heterogeneous Hardware

et al. 2022

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High-performance sensing and control systems have an important role in Industry 4.0. However, with the current solutions, the development effort is high and requires specialized skills in electronic engineering. Therefore, a model-based approach on control and signal processing systems using affordable heterogeneous hardware is proposed. In this work, a model-based code generator is developed to abstract the user from the actual software implementation. Starting from a combined model of a timing diagram and an embedded platform, a model transformation is used to automatically generate functional acquisition firmware. This firmware generator enables system engineers without deep software and hardware knowledge to set up complex control systems. Furthermore, it equips software engineers with a solid framework for faster development.

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Distributed low-cost microphone array for 3D localization for bio-acoustic applications

Cited by 5 publications

References 7 publications

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

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

Automated Firmware Generation for Compressive Sensing on Heterogeneous Hardware

Model-Based Firmware Generation for Acquisition Systems Using Heterogeneous Hardware

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