Multiple snapshot grid free compressive beamforming

Chu

et al. 2019

Sci Rep

Compressive beamforming is a powerful approach for the direction-of-arrival (DOA) estimation and strength quantification of acoustic sources. The conventional grid-based discrete compressive beamformer suffers from the basis mismatch conundrum. Its result degrades under the situation that sources fall off the grid. The existing continuous compressive beamformer with linear or planar microphone arrays can circumvent the conundrum, but work well only for sources in a local region. Here we develop a panoramic continuous compressive beamformer with cuboid microphone arrays based on an atomic norm minimization (ANM) and a matrix pencil and paring method. To solve the positive semidefinite programming equivalent to the ANM efficiently, we formulate a solving algorithm based on the alternating direction method of multipliers. We also present an iterative reweighted ANM to enhance sparsity and resolution. The beamformer is capable of estimating the DOAs and quantifying the strengths of acoustic sources panoramically and accurately, whether a standard uniform or a sparse cuboid microphone array is utilized.

Section: Introductionmentioning

confidence: 58%

“…Treating the DOAs of sources as a continuum, these beamformer can sidestep the basis mismatch conundrum fundamentally. Moreover, the multiple-snapshot data help to obtain more accurate and robust results compared with the single-snapshot ones 16,20 . In the linear array, there are only a line of microphones.…”

Section: Introductionmentioning

confidence: 99%

A panoramic continuous compressive beamformer with cuboid microphone arrays

Chu

et al. 2019

Sci Rep

“…To find the arrivals in the continuous time domain, the total variation (TV) norm (or atomic norm) was introduced. This corresponds to the l 1 -norm in the discrete time domain and measures the sparsity of the continuous signal [ 13 , 16 , 17 ]. The sparsity was imposed on the CIR

with the TV norm, while it is expressed as

in the frequency domain.…”

Section: Doa Detection In the Time Domain Using Single Measurementmentioning

confidence: 99%

“…When the actual DOA does not match one of the predefined arrival angles (basis mismatch), it is detrimental to compressive beamforming performance [ 7 , 11 ]. To mitigate basis mismatch, grid-free CS adopting atomic norm with the dual problem [ 12 , 13 , 14 , 15 ] is applied for beamforming [ 16 , 17 ]. Then, the higher resolution DOAs are found in the continuous angle domain.…”

Section: Introductionmentioning

confidence: 99%

Detection of Direction-Of-Arrival in Time Domain Using Compressive Time Delay Estimation with Single and Multiple Measurements

Choo

Park

Seong

2020

Sensors

Self Cite

The compressive time delay estimation (TDE) is combined with delay-and-sum beamforming to obtain direction-of-arrival (DOA) estimates in the time domain. Generally, the matched filter that detects the arrivals at the hydrophone is used with beamforming. However, when the ocean noise smears the arrivals, ambiguities appear in the beamforming results, degrading the DOA estimation. In this work, compressive sensing (CS) is applied to accurately evaluate the arrivals by suppressing the noise, which enables the correct detection of arrivals. For this purpose, CS is used in two steps. First, the candidate time delays for the actual arrivals are calculated in the continuous time domain using a grid-free CS. Then, the dominant arrivals constituting the received signal are selected by a conventional CS using the time delays in the discrete time domain. Basically, the compressive TDE is used with a single measurement. To further reduce the noise, common arrivals over multiple measurements, which are obtained using the extended compressive TDE, are exploited. The delay-and-sum beamforming technique using refined arrival estimates provides more pronounced DOAs. The proposed scheme is applied to shallow-water acoustic variability experiment 15 (SAVEX15) measurement data to demonstrate its validity.

“…Both these strategies are based on the single-snapshot data model and thus suitable to identify the transient or the moving sources. In 2018, Park et al [17] extended Xenaki and Gerstoft's strategy into the multiple-snapshot case through a group atomic norm. e same year, the authors [18] realize the two-dimensional multiple-snapshot grid-free compressive beamforming based on the minimization of the atomic norm of multiple-snapshot microphone pressure induced by sources and the matrix pencil and pairing (MaPP) method [19].…”

Section: Introductionmentioning

confidence: 99%

Two-Dimensional Multiple-Snapshot Grid-Free Compressive Beamforming Using Alternating Direction Method of Multipliers

Chu

2020

Shock and Vibration

Compressive beamforming with planar microphone arrays is capable of estimating the two-dimensional direction-of-arrivals (DOAs) and quantifying the strengths of acoustic sources effectively. The multiple-snapshot grid-free method has recently been concerned due to the advantages that it can circumvent the basis mismatch conundrum of the conventional grid-based method and improve the performance of the single-snapshot grid-free method. The existing atomic norm minimization based strategy uses an off-the-peg interior point method (IPM) based solver to solve the positive semidefinite programming equivalent to the atomic norm minimization. We present an alternative algorithm based on alternating direction method of multipliers (ADMM) in this paper. Both simulations and experiments demonstrate that whether a standard uniform rectangular array or a non-uniform array constituted by a small number of microphones is employed, the two-dimensional multiple-snapshot grid-free compressive beamforming using our ADMM based algorithm can estimate the DOAs and quantify the strengths of acoustic sources well, and reaching the same or even better DOA estimation accuracy as the one using the IPM based solver, our ADMM based algorithm is distinctly faster.