Abstract
A major limitation of most methods exploiting sparse signal or spectral models for the purpose of estimating directions-of-arrival stems from the fixed model dictionary that is formed by array response vectors over a discrete search grid of possible directions. In general, the array responses to actual DoAs will most likely not be members of such a dictionary. In this work, the sparse spectral signal model with uncertainty of linearized dictionary parameter mismatch is considered, and the dictionary matrix is reformulated into a multiplication of a fixed base dictionary and a sparse matrix. Based on this double-sparsity model, an alternating dictionary learning-sparse spectral model fitting approach is proposed to reduce the estimation errors of DoAs and their powers. Group-sparsity estimator and Lasso-based Least Squares are utilized in the formulation of the associated optimization problem. The performance of the proposed methods are demonstrated by numerical simulations.
Original language | English (US) |
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Title of host publication | 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop, SAM 2014 |
Publisher | IEEE Computer Society |
Pages | 377-380 |
Number of pages | 4 |
ISBN (Print) | 9781479914814 |
DOIs | |
State | Published - Jan 1 2014 |
Event | 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop, SAM 2014 - A Coruna, Spain Duration: Jun 22 2014 → Jun 25 2014 |
Other
Other | 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop, SAM 2014 |
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Country/Territory | Spain |
City | A Coruna |
Period | 6/22/14 → 6/25/14 |