Charrelation-assisted covariance fitting

Covariance fitting is a commonly used approach in array processing for estimating the power of signals impinging on a sensors array, and/or for refining estimates of the array's steering vectors. In this work we consider the possibility to further refine these estimates using a recently propose...

Full description

Saved in:
Bibliographic Details
Main Authors: Yeredor, Arie, Slapak, A.
Format: Conference Proceeding
Language:English
Subjects:
Array signal processing
Covariance matrix
Fitting
Matrix decomposition
Noise
Performance analysis
Vectors
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Covariance fitting is a commonly used approach in array processing for estimating the power of signals impinging on a sensors array, and/or for refining estimates of the array's steering vectors. In this work we consider the possibility to further refine these estimates using a recently proposed generic statistic - called the Charrelation matrix, similar in form and in structure to the covariance matrix, but generally carrying information beyond second-order. The charrelation matrix and the statistics of its sample-estimate depend on the selection of a parameters-vector called "processing-point". As we show in here, the use of charrelation matrices taken at one or more processing-points as a substitute to the covariance (which is the charrelation matrix taken at an all-zeros processing-point), can yield significant improvement in the resulting estimates of the steering-vectors.
DOI:10.1109/EEEI.2012.6376997