Reference : A Statistically Efficient Estimator for Co-array Based DoA Estimation
Scientific congresses, symposiums and conference proceedings : Poster
Engineering, computing & technology : Electrical & electronics engineering
http://hdl.handle.net/10993/37660
A Statistically Efficient Estimator for Co-array Based DoA Estimation
English
Sedighi, Saeid mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Shankar, Bhavani mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Ottersten, Björn mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Oct-2018
Yes
International
The Asilomar Conference on Signals, Systems, and Computers
28-10-108 to 31-10-2018
[en] Co-array Based DoA Estimation ; Sparse arrays ; Cramer-Rao Bound (CRB)
[en] Co-array-based Direction of Arrival (DoA) estimation using Sparse linear arrays (SLAs) has recently gained considerable interest in array processing due to the attractive capability of providing enhanced degrees of freedom. Although a variety of estimators have been suggested in the literature for co-array-based DoA estimation, none of them are statistically efficient. This work introduces a novel Weighted Least Squares (WLS) estimator for the co-array-based DoA estimation employing the covariance fitting method. Then, an optimal weighting is given so that the asymptotic performance of the proposed WLS estimator coincides with the Cram\'{e}r-Rao Bound (CRB), thereby ensuring statistical efficiency of resulting WLS estimator. This implies that the proposed WLS estimator has significantly better performance compared to existing methods in the literature. Numerical simulations are provided to corroborate the asymptotic statistical efficiency and the improved performance of the proposed estimator.
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/37660
FnR ; FNR11228830 > Saeid Sedighi > > Compressive Sensing for Ranging and Detection in Automotive Applications > 15/02/2017 > 14/02/2021 > 2016

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