DMCA
A Sparse Signal Reconstruction Perspective for Source Localization With Sensor Arrays (2005)
Cached
Download Links
- [ssg.mit.edu]
- [ssg.mit.edu]
- [ssg.mit.edu]
- [ssg.mit.edu]
- DBLP
Other Repositories/Bibliography
| Citations: | 229 - 6 self |
BibTeX
@MISC{Malioutov05asparse,
author = {Dmitry Malioutov and Müjdat Çetin and Alan S. Willsky},
title = {A Sparse Signal Reconstruction Perspective for Source Localization With Sensor Arrays},
year = {2005}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
We present a source localization method based on a sparse representation of sensor measurements with an overcomplete basis composed of samples from the array manifold. We enforce sparsity by imposing penalties based on the 1-norm. A number of recent theoretical results on sparsifying properties of 1 penalties justify this choice. Explicitly enforcing the sparsity of the representation is motivated by a desire to obtain a sharp estimate of the spatial spectrum that exhibits super-resolution. We propose to use the singular value decomposition (SVD) of the data matrix to summarize multiple time or frequency samples. Our formulation leads to an optimization problem, which we solve efficiently in a second-order cone (SOC) programming framework by an interior point implementation. We propose a grid refinement method to mitigate the effects of limiting estimates to a grid of spatial locations and introduce an automatic selection criterion for the regularization parameter involved in our approach. We demonstrate the effectiveness of the method on simulated data by plots of spatial spectra and by comparing the estimator variance to the Cramér–Rao bound (CRB). We observe that our approach has a number of advantages over other source localization techniques, including increased resolution, improved robustness to noise, limitations in data quantity, and correlation of the sources, as well as not requiring an accurate initialization.
Keyphrases
source localization sparse signal reconstruction perspective sensor array sparse representation spatial spectrum second-order cone automatic selection criterion interior point implementation overcomplete representation sensor measurement multiple time cram rao bound data matrix regularization parameter estimator variance accurate initialization sensor array processing index term direction-of-arrival estimation optimization problem source localization method source localization technique array manifold spatial location grid refinement method data quantity recent theoretical result sharp estimate frequency sample overcomplete basis singular value decomposition
