Paper Info
Title
Knowledge-aided direction finding based on Unitary ESPRIT
Abstract
In certain applications involving direction finding, a priori knowledge of a subset of the directions to be estimated is sometimes available. Existing knowledge-aided (KA) methods apply projection and polynomial rooting techniques to exploit this information in order to improve the estimation accuracy of the unknown signal directions. In this paper, a new strategy for incorporating prior knowledge is developed for situations with a low signal-to-noise ratio (SNR) and a limited data record based on the Unitary ESPRIT algorithm. The proposed KA-Unitary ESPRIT algorithm processes an enhanced covariance matrix estimate obtained by applying a shrinkage covariance estimator, which linearly combines the sample covariance matrix and an a priori known covariance matrix in an automatic fashion. Simulations show that the derived algorithm achieves significant performance gains in estimating the unknown sources and additionally provides a high robustness in the case of inaccurate prior knowledge.
Year
DOI
Venue
2011
10.1109/ACSSC.2011.6190075
Signals, Systems and Computers
Keywords
DocType
ISSN
covariance matrices,direction-of-arrival estimation,polynomials,signal processing,DOA estimation,KA-Unitary ESPRIT algorithm,SNR,covariance matrix estimates,data record,direction of arrival estimation,knowledge-aided direction finding,performance gains,polynomial rooting technique,projection technique,shrinkage covariance estimator,signal-to-noise ratio,unknown signal direction estimation accuracy improvement,Direction of arrival (DOA) estimation,Unitary ESPRIT,prior knowledge,shrinkage covariance estimator
Conference
1058-6393
ISBN
Citations 
PageRank 
978-1-4673-0321-7
2
0.36
References 
Authors
0
3
Name
Order
Citations
PageRank
Jens Steinwandt120.36
de Lamare, R.C.265233.42
Martin Haardt33531311.32