Compressive Sensing for Near-field Source Localization

Near-field source localization is an important aspect in many diverse areas such as acoustics, seismology, to list a few. The planar wave assumption frequently used in far-field source localization is no longer valid when the sources are in the near field. Near-field sources can be localized by solving a joint direction-of-arrival and range estimation problem. The original near-field source localization problem is a multi-dimensional non-linear optimization problem which is computationally intractable. In this thesis we study address two important questions related to near-field source localization: (i) Sparse reconstruction techniques for joint DOA and range estimation using a grid-based model. (ii) Matching the sampling grid for off-grid sources. In the first part of this thesis, we use a grid-based model and by further leveraging the sparsity, we can solve the aforementioned problem efficiently using any of the off-the-shelf l1_-norm optimization solvers. When multiple snapshots are available, we can also exploit the cross-correlations among the symmetric sensors of the array and further reduce the complexity by solving two sparse reconstruction problems of lower dimensions instead of a single sparse reconstruction problem of a higher dimension. In the second part of this thesis, we account scenarios where the true source locations are not on the grid resulting in a grid mismatch. Using the first-order Taylor approximation, we model the grid mismatch as a perturbation around the sampling grid. Based on the grid mismatch model, we propose a bounded sparse and bounded joint sparse recovery algorithms to localize near-field sources.

Subject

Near-field source localization
compressive sensing
Fresnel approximation
correlation
sparse modeling
joint sparse recovery
EIV model

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http://resolver.tudelft.nl/uuid:f0e70c18-52d1-4c9b-a611-dbd534a0190c

Embargo date

2014-02-15

Part of collection

Student theses

Document type

master thesis

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