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Shift invariance, incomplete arrays and coupled CPD: A case study | IEEE Conference Publication | IEEE Xplore

Shift invariance, incomplete arrays and coupled CPD: A case study


Abstract:

Tensors have proven to be useful tools for array processing. Most attention has been paid to separable arrays, which lead to a Canonical Polyadic Decomposition (CPD). For...Show More

Abstract:

Tensors have proven to be useful tools for array processing. Most attention has been paid to separable arrays, which lead to a Canonical Polyadic Decomposition (CPD). For more general geometries, and in particular for sparse arrays and arrays with missing sensors, more general tensor methods are required. The recently proposed coupled CPD framework allows a data fission/fusion approach in which one zooms in on partial structures and combines the partial CPDs through which the latter are imposed. This approach yields explicit algebraic conditions under which the solution is unique. The exact solution can be found with a matrix eigenvalue decomposition in the noiseless case, similar to ESPRIT in the case of uniform linear arrays. We study in detail the case of sparse spatial sampling where sensors are located on points of a two-dimensional grid. Despite the fact that the array is incomplete, coupled CPD allows us to exploit the rectangularity of the grid as well as the uniformity of the spatial sampling in both dimensions.
Date of Conference: 10-13 July 2016
Date Added to IEEE Xplore: 19 September 2016
ISBN Information:
Electronic ISSN: 2151-870X
Conference Location: Rio de Janeiro, Brazil

References

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