On the Design of Sparse Arrays With Frequency-Invariant Beam Pattern

Beamformer performs spatial filtering to preserve the desired signal while suppressing interfering signals and noise arriving from directions other than the direction of interest. However, the beam pattern of the conventional beamformer is dependent on the frequency of the signal. It is common to us...

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Bibliographic Details
Published in:IEEE/ACM transactions on audio, speech, and language processing speech, and language processing, 2021, Vol.29, p.226-238
Main Author: Son, Phan Le
Format: Article
Language:English
Subjects:
Acoustic beams
Array signal processing
Arrays
Beamforming
Broadband
Clustering
Directivity
frequency-invariant beamformer
Indexes
Invariants
Microphone arrays
Optimization
Optimization techniques
Sensor arrays
Sensors
sparse design
Spatial filtering
Transmission line matrix methods
Weight
White noise
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Summary:Beamformer performs spatial filtering to preserve the desired signal while suppressing interfering signals and noise arriving from directions other than the direction of interest. However, the beam pattern of the conventional beamformer is dependent on the frequency of the signal. It is common to use dense and uniform arrays for a broadband signal to achieve some essential performances together, such as frequency-invariant, white noise gain, directivity factor, front-to-back ratio, etc. Recently, the interest in sparse arrays is growing, mainly due to the capacity to reduce the number of sensors. Nevertheless, in general, finding a suitable sparse array layout is still a challenging task. Many studies have focused on optimization procedures to seek the sparse array deployment. This paper presents an alternative approach to determine the location of sensors. Starting with a weight spectrum of a virtual uniform array, some techniques are used, such as analyzing the weight spectrum to determine the critical sensors, applying the clustering technique to group the sensors into the different groups, and selecting the representative sensors for each group. After the sparse array deployment is specified, the optimization technique is applied to find the beamformer coefficients. The proposed method helps to save the computation time in the design phase, and its beamformer performance outperforms other state-of-the-art methods in several aspects.
ISSN:2329-9290
2329-9304
DOI:10.1109/TASLP.2020.3040033