NDLI: An efficient signal subspace algorithm for source localization in noise fields with unknown covariance

Content Provider	IEEE Xplore Digital Library
Author	Wiliams, R.T. Mahalanabis, A.K. Sibul, L.H. Prasad, S.
Copyright Year	1988
Description	Author affiliation: Pennsylvania State Univ., University Park, PA, USA (Wiliams, R.T.; Mahalanabis, A.K.; Sibul, L.H.; Prasad, S.)
Abstract	The authors present a covariance differencing algorithm for bearing estimation in situations where the noise covariance matrix is unknown. Conventional covariance differencing methods acquire a set of vectors which are orthogonal to the direction vectors by obtaining an eigenvalue decomposition of the difference of the covariance matrices of two measurements of the array. Eigendecomposition algorithms, however, involve a considerable computational burden. The authors also consider a procedure which does not require eigenvalue decomposition and is thus computationally more efficient. Results of simulation studies are included to show that the proposed approach performs nearly as well as the more conventional covariance differencing techniques in terms of signal resolution and estimation error.<>
Starting Page	2829
Ending Page	2832
File Size	308445
Page Count	4
File Format	PDF
ISSN	15206149
DOI	10.1109/ICASSP.1988.197241
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1988-04-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sensor arrays Covariance matrix Noise measurement Noise robustness Signal processing Equations Matrix decomposition Direction of arrival estimation Eigenvalues and eigenfunctions Computational modeling
Content Type	Text
Resource Type	Article

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