Single-snapshot DOA estimation by using Compressed Sensing
Stefano Fortunati,Raffaele Grasso,Fulvio Gini,Maria Greco,Kevin D. LePage +4 more
- Vol. 2014, Iss: 1, pp 120
TLDR
Theoretical arguments and simulation analysis provide evidence that a CS-based beamformer can achieve resolution beyond the classical Rayleigh limit and the theoretical findings are validated by processing a real sonar dataset.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 l1 minimization (or Least Absolute Shrinkage and Selection Operator, LASSO), the fast smooth l0 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.read more
Citations
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Advances in Automotive Radar: A framework on computationally efficient high-resolution frequency estimation
Florian Engels,Philipp Heidenreich,Abdelhak M. Zoubir,Friedrich K. Jondral,Markus Wintermantel +4 more
TL;DR: A flexible framework for computationally efficient high-resolution frequency estimation based on decoupled frequency estimation in the Fourier domain, where high- resolution processing can be applied to either the range, relative velocity, or angular dimension is presented.
Journal ArticleDOI
Multiple and single snapshot compressive beamforming
TL;DR: In this paper, compressive sensing (CS) is used to reconstruct the direction of arrival (DOA) of multiple sources using a sparsity constraint, where the acoustic pressure at each sensor is expressed as a phase-lagged superposition of source amplitudes at all hypothetical DOAs.
Journal ArticleDOI
Weighted SPICE: A unifying approach for hyperparameter-free sparse estimation☆
TL;DR: This paper establishes a connection between SPICE and the l 1 -penalized LAD estimator as well as the square-root LASSO method and evaluates the four methods mentioned above in a generic sparse regression problem and in an array processing application.
Journal ArticleDOI
Block-sparse beamforming for spatially extended sources in a Bayesian formulation
TL;DR: Simulations and experimental measurements show that a composite prior is introduced, which simultaneously promotes a piecewise constant profile and sparsity in the solution, provides high-resolution DOA estimation in a general framework, i.e., in the presence of spatially extended sources.
Journal ArticleDOI
Adaptive and compressive matched field processing
TL;DR: Compressive sensing (CS) implemented using basis pursuit is reformulated as an underdetermined, convex optimization problem, demonstrating it is robust to data-replica mismatch.
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