368 research outputs found
Multiple and single snapshot compressive beamforming
For a sound field observed on a sensor array, compressive sensing (CS)
reconstructs the direction-of-arrival (DOA) of multiple sources using a
sparsity constraint. The DOA estimation is posed as an underdetermined problem
by expressing the acoustic pressure at each sensor as a phase-lagged
superposition of source amplitudes at all hypothetical DOAs. Regularizing with
an -norm constraint renders the problem solvable with convex
optimization, and promoting sparsity gives high-resolution DOA maps. Here, the
sparse source distribution is derived using maximum a posteriori (MAP)
estimates for both single and multiple snapshots. CS does not require inversion
of the data covariance matrix and thus works well even for a single snapshot
where it gives higher resolution than conventional beamforming. For multiple
snapshots, CS outperforms conventional high-resolution methods, even with
coherent arrivals and at low signal-to-noise ratio. The superior resolution of
CS is demonstrated with vertical array data from the SWellEx96 experiment for
coherent multi-paths.Comment: In press Journal of Acoustical Society of Americ
Grid-free compressive beamforming
The direction-of-arrival (DOA) estimation problem involves the localization
of a few sources from a limited number of observations on an array of sensors,
thus it can be formulated as a sparse signal reconstruction problem and solved
efficiently with compressive sensing (CS) to achieve high-resolution imaging.
On a discrete angular grid, the CS reconstruction degrades due to basis
mismatch when the DOAs do not coincide with the angular directions on the grid.
To overcome this limitation, a continuous formulation of the DOA problem is
employed and an optimization procedure is introduced, which promotes sparsity
on a continuous optimization variable. The DOA estimation problem with
infinitely many unknowns, i.e., source locations and amplitudes, is solved over
a few optimization variables with semidefinite programming. The grid-free CS
reconstruction provides high-resolution imaging even with non-uniform arrays,
single-snapshot data and under noisy conditions as demonstrated on experimental
towed array data.Comment: 14 pages, 8 figures, journal pape
Direction of arrival estimation using robust complex Lasso
The Lasso (Least Absolute Shrinkage and Selection Operator) has been a
popular technique for simultaneous linear regression estimation and variable
selection. In this paper, we propose a new novel approach for robust Lasso that
follows the spirit of M-estimation. We define -Lasso estimates of regression
and scale as solutions to generalized zero subgradient equations. Another
unique feature of this paper is that we consider complex-valued measurements
and regression parameters, which requires careful mathematical characterization
of the problem. An explicit and efficient algorithm for computing the -Lasso
solution is proposed that has comparable computational complexity as
state-of-the-art algorithm for computing the Lasso solution. Usefulness of the
-Lasso method is illustrated for direction-of-arrival (DoA) estimation with
sensor arrays in a single snapshot case.Comment: Paper has appeared in the Proceedings of the 10th European Conference
on Antennas and Propagation (EuCAP'2016), Davos, Switzerland, April 10-15,
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