Low-cost one-bit MEMS microphone arrays for in-air acoustic imaging using FPGA's

Kerstens, Robin; Laurijssen, Dennis; Steckel, Jan

doi:10.1109/icsens.2017.8234087

Cited by 14 publications

(9 citation statements)

References 4 publications

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“…objects in front of the sensor, a 32-element microphone array is used. This array consists out of 32 microphones with a built-in Σ∆-ADC that uses Pulse-Density-Modulation (PDM) [12]. The benefit of this is that no amplification of the microphone signal is necessary and through which analog noise which is added by the circuitry between the microphone, the amplifier and the digitizer is minimised.…”

Section: Sensor Architecturementioning

confidence: 99%

“…This signal will be reflected by objects in the environment. As the acoustic energy of the transducer is distributed quite broadly in the frontal hemisphere, and the microphones are near omnidirectional, the field-of-view of the sensor consists of the frontal hemisphere [9] [12]. These reflections will then be received by each microphone in the 32-element array individually.…”

Section: Signal Processingmentioning

confidence: 99%

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eRTIS: A Fully Embedded Real Time 3D Imaging Sonar Sensor for Robotic Applications

Kerstens

Laurijssen

Steckel

2019

2019 International Conference on Robotics and Automation (ICRA)

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Many popular advanced sonar systems provide accurate and reliable measurements containing crucial info needed by robotic applications such as range, bearing and reflection strength of the objects in the field of view. While these sensor systems provide these crucial pieces of information accurately, they are often limited by a lack of processing power and/or size which leads to them needing an external computing device to process all the information generated by the microphone array on the sensor. In this paper we present two versions of a novel fully embedded 3D sonar sensor which have different sensing architectures which enable 3D perception for robotic application in harsh conditions using ultrasound at low cost. Experimental results taken from an office environment will show the 3D localization capabilities and performance of the sensor, showing the sensor has a large field-of-view (FoV) with accurate 3D localization combined with real-time capabilities.

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Section: Sensor Architecturementioning

confidence: 99%

Section: Signal Processingmentioning

confidence: 99%

eRTIS: A Fully Embedded Real Time 3D Imaging Sonar Sensor for Robotic Applications

Kerstens

Laurijssen

Steckel

2019

2019 International Conference on Robotics and Automation (ICRA)

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“…Given our previous work [24]- [26], accurately measuring distance using ultrasound emissions is a feasible and low-cost solution. Therefore, a three-element digital microphone array [29] was fitted on the nodes. These Knowles SPH0641LU4H-1 [30] digital microphones incorporate an analog amplifier together with a 1-bit Analog-to-Digital Converter (ADC) in a single cost-efficient package.…”

Section: A Hardware Architecturementioning

confidence: 99%

Synchronous Wireless Body Sensor Network Enabling Human Body Pose Estimation

et al. 2019

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In this paper, a novel human body pose estimation system is introduced, which features a synchronous network of wirelessly connected sensor nodes, thus forming a wireless body sensor network (WBSN). This measurement architecture for establishing human body pose estimations works in combination with the HTC Vive base stations and a Wi-Fi router. The former emits infrared pulses and laser sweeps in the horizontal and vertical plane captured by the sensor nodes' photodiodes. The infrared data can be converted into an azimuth and elevation angle for each photodiode relative to the emitting HTC Vive device. To estimate the human body poses, the sensor data are fed into a probabilistic non-linear maximum likelihood estimator combined with a parametric human body model. Using a parametric human body model for estimating the poses proves to be more resilient to sensor noise compared to estimating the Six Degrees of Freedom (6DoF) of every sensor node individually. The solution space can be constrained to the parameters of the users body model that relates to a priori information on the subject. The combination of the proposed hardware sensor network, its synchronous sensor data, and processing algorithms yields a cost-efficient human body pose estimation system.INDEX TERMS Body sensor networks, distributed embedded systems, motion capture, pose estimation, sensor arrays.

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“…12 Hz, which can be considered slow in comparison to its non-acoustical alternatives. The only way to receive more information about the environment has been to increase the number of recorded data points by either upgrading the hardware to get an increase in processing power and in turn, frame rate [3], [4] or to simply increase the number of sensors used [5], [6]. Both of these ways require inherent changes VOLUME 4, 2016 and increased cost to the sensor hardware that is being used, which limits the practicality of these improvements in terms of development.…”

Section: Introductionmentioning

confidence: 99%

Radial Velocity Estimation for Multiple Objects Using In-Air Sonar With MIMO Virtual Arrays

2023

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As autonomous platforms become more advanced, it is useful to obtain more information about the environment. Classic vision sensors such as RGB-D cameras, LiDAR, or acoustical imaging cameras are capable of accurately displaying the location of an object in 3D space and are widely used for this purpose. However, a problem arises when the measurement environment becomes more complex and measurement data might begin to suffer in terms of accuracy. Imaging sonar sensors are developed specifically for these environments but are limited in terms of frame rate due to the inherent slow nature of sound. This paper will propose a method to increase the amount of useful information obtained from the available data from a single measurement in post-processing. Utilizing a signal in combination with a Doppler velocity-tuned matched-filter bank it is possible to estimate the radial velocity of an object with respect to the sensor's location. This allows the robot to make better decisions when it comes to path planning and collision avoidance, as a rapidly approaching object requires a different action than a stationary object or one moving away from the sensor. Another advantage of this system is that a single seeminglylarge object might be identified as two close lying objects with different speeds. This paper serves as a proof-of-concept with results from a realistic simulation environment using an imaging sonar sensor and shows that the proposed method is perfectly suitable for making accurate estimations of an object's radial velocity.

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Low-cost one-bit MEMS microphone arrays for in-air acoustic imaging using FPGA's

Cited by 14 publications

References 4 publications

eRTIS: A Fully Embedded Real Time 3D Imaging Sonar Sensor for Robotic Applications

eRTIS: A Fully Embedded Real Time 3D Imaging Sonar Sensor for Robotic Applications

Synchronous Wireless Body Sensor Network Enabling Human Body Pose Estimation

Radial Velocity Estimation for Multiple Objects Using In-Air Sonar With MIMO Virtual Arrays

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