Paper Info
Title
Characterization of orthogonal subspaces for alias-free reconstruction of damped complex exponential modes in sparse arrays
Abstract
We consider the problem of estimating p damped complex exponentials from spatial samples of their weighted sum, taken by a sparse sensor array. Our focus is on a particular sparse array geometry, where the array can be thought of as a subsampled version of a dense (with half-wavelength spacings) uniform line array, plus an extra sensor that is posited at a location on the array that allows us to resolve aliasing ambiguities. This array geometry is a special, but canonical, example of a co-prime sensor array. Our main result is a 2p-parameter characterization of the so-called orthogonal subspace. This is the subspace that is orthogonal to the subspace spanned by the columns of the generalized Vandermonde matrix of modes in the sparse array. This characterization allows us to extend methods of linear prediction and approximate least squares, such as iterative quadratic maximum likelihood (IQML), for estimating mode parameters.
Year
DOI
Venue
2014
10.1109/ACSSC.2014.7094405
ACSSC
Keywords
Field
DocType
iterative quadratic maximum likelihood,array geometry,parameter estimation,alias-free reconstruction,matrix algebra,sparse sensor array,aliasing ambiguity,array signal processing,linear prediction method,least squares approximations,least square approximation,2p-parameter characterization,vandermonde matrix,damped complex exponential mode,signal reconstruction,orthogonal subspace characterization,uniform line array,coprime sensor array,iqml
Least squares,Mathematical optimization,Sparse array,Subspace topology,Sensor array,Linear prediction,Linear subspace,Aliasing,Vandermonde matrix,Mathematics
Conference
ISSN
Citations 
PageRank 
1058-6393
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Pooria Pakrooh1445.24
Ali Pezeshki245038.31
Louis L. Scharf32525414.45