In:
EURASIP Journal on Advances in Signal Processing, Springer Science and Business Media LLC, Vol. 2014, No. 1 ( 2014-12)
Abstract:
This paper deals with the problem of estimating the directions of arrival (DOA) of multiple source signals from a single observation vector of an array data. In particular, four estimation algorithms based on the theory of compressed sensing (CS), i.e., the classical ℓ 1 minimization (or Least Absolute Shrinkage and Selection Operator, LASSO), the fast smooth ℓ 0 minimization, and the Sparse Iterative Covariance-Based Estimator, SPICE and the Iterative Adaptive Approach for Amplitude and Phase Estimation, IAA-APES algorithms, are analyzed, and their statistical properties are investigated and compared with the classical Fourier beamformer (FB) in different simulated scenarios. We show that unlike the classical FB, a CS-based beamformer (CSB) has some desirable properties typical of the adaptive algorithms (e.g., Capon and MUSIC) even in the single snapshot case. Particular attention is devoted to the super-resolution property. Theoretical arguments and simulation analysis provide evidence that a CS-based beamformer can achieve resolution beyond the classical Rayleigh limit. Finally, the theoretical findings are validated by processing a real sonar dataset.
Type of Medium:
Online Resource
ISSN:
1687-6180
DOI:
10.1186/1687-6180-2014-120
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2014
detail.hit.zdb_id:
2364203-8
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