Coprime sampling for nonstationary signal in radar signal processing

Wu, Qiong; Liang, Qilian

doi:10.1186/1687-1499-2013-58

Cited by 28 publications

(11 citation statements)

References 16 publications

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“…[10][11][12] This letter reports on the experimental validation conducted using a linear coprime array consisting of two subarrays of nine and eight microphones in a well-controlled quasi-anechoic environment. This letter discusses preliminary results.…”

Section: Introductionmentioning

confidence: 99%

Experimental validation of a coprime linear microphone array for high-resolution direction-of-arrival measurements

Xiang

Bush

Summers

2015

The Journal of the Acoustical Society of America

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Coprime linear microphone arrays allow for narrower beams with fewer sensors. A coprime microphone array consists of two staggered uniform linear subarrays with M and N microphones, where M and N are coprime with each other. By applying spatial filtering to both subarrays and combining their outputs, M+N−1 microphones yield M⋅N directional bands. In this work, the coprime sampling theory is implemented in the form of a linear microphone array of 16 elements with coprime numbers of 9 and 8. This coprime microphone array is experimentally tested to validate the coprime array theory. Both predicted and measured results are discussed. Experimental results confirm that narrow beampatterns as predicted by the coprime sampling theory can be obtained by the coprime microphone array.

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Section: Introductionmentioning

confidence: 99%

Experimental validation of a coprime linear microphone array for high-resolution direction-of-arrival measurements

Xiang

Bush

Summers

2015

The Journal of the Acoustical Society of America

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“…Thus, for a full input of 50000 points, we repeat the reconstruction process 390 times. We decide to take our measurements via a coprime sampling technique -a method studied in the recent years for signal processing with promising results [16] [17]. Initially, we set the coprime values to 5 and 7 in order to obtain 322 measurements -corresponding to roughly 30% of data from the original The results that we will first display and comment in this paper are done accordingly to the above settings and will demonstrate two points from the three ones mentioned in this paper: the compromise between processing time and accuracy and the influence of the number of measurements taken.…”

Section: Resultsmentioning

confidence: 99%

ADMM-based audio reconstruction for low-cost-sound-monitoring

Ramaswami

Kawaguchi

Takashima

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-For low-cost sound monitoring of machineries, we propose a novel audio reconstruction method superior in terms of accuracy and processing time. A conventional method based on the Orthogonal Matching Pursuit (OMP) has been proposed for audio recovery. However, the conventional method has a low performance for sounds of machineries because, sounds of machineries tend to be not highly sparse, and the reconstruction performance of OMP decreases extremely if the signal is not sufficiently sparse. To solve the problem of the conventional method, the proposed method is based on the Alternating Direction Method of Multipliers (ADMM) for Group Lasso combined with the Gabor dictionary. While OMP's performance decreases with the number of nonzero elements, the proposed method shows a better robustness to variations in sparsity and outputs a reasonable result in a few tens of iterations. Those features among others make the algorithm a reliable solution which offers a better trade-off between accuracy and processing time compared to the conventional method.

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“…[6][7][8][9][10][11] This includes the authors' investigations into expanding the coprime theory to include alternate operating frequencies. 12 The results showed that coprime arrays provide useful results at any frequency lower than the design frequency implied by the length and two coprime factors of the standard CSA.…”

Section: Introductionmentioning

confidence: 99%

n-tuple coprime sensor arrays

Bush

Xiang

2017

The Journal of the Acoustical Society of America

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Until now, coprime sensor arrays have used two sparsely spaced subarrays to emulate the performance of a single uniform array with many more sensors (generally on the order of the product of each subarrays' number of sensors). This allows for similar results with fewer sensors, or the observation of higher frequencies (above the Nyquist limit) with a similar number of sensors. The theory rests on the cross-referencing (using directional filter banks) or cancellation (using product processing) of the M grating lobes in one subarray's beampattern and N grating lobes in the other, where M and N are coprime integers. Sets of coprime integers can consist of more than two integers, however, and introducing another coprime factor theoretically multiplies observable frequency (or further decreases the number of array elements needed for the same frequency). Any amount, n, of coprime integers and corresponding subarrays may be used. In this work, “n-tuple coprime sensor array” theory is expounded and implemented. Experimentally measured beampattern results of a triple coprime sensor array (with three subarrays) are shown, using an extension of the authors' previously established product processing. Results also confirm that the usable range of an n-tuple coprime array extends below its design frequency.

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Coprime sampling for nonstationary signal in radar signal processing

Cited by 28 publications

References 16 publications

Experimental validation of a coprime linear microphone array for high-resolution direction-of-arrival measurements

Experimental validation of a coprime linear microphone array for high-resolution direction-of-arrival measurements

ADMM-based audio reconstruction for low-cost-sound-monitoring

n-tuple coprime sensor arrays

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