Localization of low-frequency coherent sound sources with compressive beamforming-based passive synthetic aperture

Lei, Zhixiong; Yang, Kunde; Duan, Rui; Xiao, Peng

doi:10.1121/1.4915003

Cited by 26 publications

(13 citation statements)

References 12 publications

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“…On the other hand, with the reverberation knowledge available in many scenarios, an acoustic imaging problem can be solved as a linear inverse problem [6][7][8]. Such inverse problems are usually underdetermined when a high imaging resolution is required, since the recording samples are usually limited compared to imaging pixels even with a synthetic aperture [9]. Therefore, prior knowledge about the source distribution is often necessary to make the ground truth estimation problem tractable or less ill-posed.…”

Section: Introductionmentioning

confidence: 99%

Synthetic Aperture Acoustic Imaging with Deep Generative Model Based Source Distribution Prior

Fan

Das

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Acoustic imaging has a wide range of real-world applications such as machine health monitoring. Conventionally, large microphone arrays are utilized to achieve useful spatial resolution in the imaging process. The advent of location-aware autonomous mobile robotic platforms opens up unique opportunity to apply synthetic aperture techniques to the acoustic imaging problem. By leveraging motion and location cues as well as some available prior information on the source distribution, a small moving microphone array has the potential to achieve imaging resolution far beyond the physical aperture limits. In this work, we propose to image large acoustic sources with a combination of synthetic aperture and their geometric structures modeled by a conditional generative adversarial network (cGAN). The acoustic imaging problem is formulated as a linear inverse problem and solved with the gradient-based method. Numerical simulations show that our synthetic aperture imaging framework can reconstruct the acoustic source distribution from microphone recordings and outperform static microphone arrays.

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Section: Introductionmentioning

confidence: 99%

Synthetic Aperture Acoustic Imaging with Deep Generative Model Based Source Distribution Prior

Fan

Das

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…The theory of CS was initially proposed for low-rate image acquisition. It was then developed for many other applications such as ultrasound imaging [ 5 ], face recognition [ 6 , 7 ], single- pixel camera [ 8 ], wireless sensors networks [ 9 , 10 ], cognitive radio networks [ 11 , 12 ], sound localization [ 13 ], audio processing [ 14 , 15 ], radar imaging [ 16 , 17 ], image processing [ 18 , 19 ], and video processing [ 20 , 21 ]. Similarly, CS has contributed to various neural engineering research including, neuronal network connectivity [ 22 ], magnetic resonance image (MRI) acquisition [ 23 ], MRI reconstruction [ 24 ], electroencephalogram (EEG) monitoring [ 25 ], compressive imaging [ 26 , 27 ], and other applications.…”

Section: Introductionmentioning

confidence: 99%

Trends in Compressive Sensing for EEG Signal Processing Applications

Gurve

Delisle-Rodríguez

Bastos

et al. 2020

Sensors

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The tremendous progress of big data acquisition and processing in the field of neural engineering has enabled a better understanding of the patient’s brain disorders with their neural rehabilitation, restoration, detection, and diagnosis. An integration of compressive sensing (CS) and neural engineering emerges as a new research area, aiming to deal with a large volume of neurological data for fast speed, long-term, and energy-saving purposes. Furthermore, electroencephalography (EEG) signals for brain–computer interfaces (BCIs) have shown to be very promising, with diverse neuroscience applications. In this review, we focused on EEG-based approaches which have benefited from CS in achieving fast and energy-saving solutions. In particular, we examine the current practices, scientific opportunities, and challenges of CS in the growing field of BCIs. We emphasized on summarizing major CS reconstruction algorithms, the sparse basis, and the measurement matrix used in CS to process the EEG signal. This literature review suggests that the selection of a suitable reconstruction algorithm, sparse basis, and measurement matrix can help to improve the performance of current CS-based EEG studies. In this paper, we also aim at providing an overview of the reconstruction free CS approach and the related literature in the field. Finally, we discuss the opportunities and challenges that arise from pushing the integration of the CS framework for BCI applications.

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“…This technique was firstly used in cooperative scenarios for radar imaging [10,11] and then extended to non-cooperative acoustic processing [12,13,14]. However, the applications of the SA technique in communication signal processing have not been addressed often.…”

Section: Introductionmentioning

confidence: 99%

A Joint Space-Time Array for Communication Signals-Based on a Moving Platform and Performance Analysis

Yang

Wang

Yin

2018

Sensors

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A joint space-time array for communication signals is constructed in this paper to settle the contradiction between the performance of angle estimation and the array aperture. It introduces Doppler information caused by platform motion into the signal processing to obtain favorable performance with limited array aperture. We analyze the theoretical performance in the aspects of distinguishable source number, spatial resolution and Cramér-Rao bound (CRB), respectively. Both theoretical analysis and simulation results demonstrate that the proposed space-time array can give rise to a significant enhancement in achievable array performance.

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Localization of low-frequency coherent sound sources with compressive beamforming-based passive synthetic aperture

Cited by 26 publications

References 12 publications

Synthetic Aperture Acoustic Imaging with Deep Generative Model Based Source Distribution Prior

Synthetic Aperture Acoustic Imaging with Deep Generative Model Based Source Distribution Prior

Trends in Compressive Sensing for EEG Signal Processing Applications

A Joint Space-Time Array for Communication Signals-Based on a Moving Platform and Performance Analysis

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