Abstract:
Standard covariance matrix estimation procedures can be very affected by either the presence of outliers in the data or some mismatch in their statistical model. In the S...Show MoreMetadata
Abstract:
Standard covariance matrix estimation procedures can be very affected by either the presence of outliers in the data or some mismatch in their statistical model. In the Spherically Invariant Random Vectors (SIRV) framework, this paper proposes the statistical analysis of the Normalized Sample Covariance Matrix (NSCM) and the Fixed Point (FP) estimates in disturbances context. The main contribution of this paper is to theoretically derive the bias of the NSCM and the FP arising from disturbances in the data used to build these estimates. The superiority of these two estimates is then highlighted in Gaussian or SIRV noise corrupted by strong deterministic disturbances. This robustness can be helpful for applications such as adaptive radar detection or sources localization methods.
Published in: 2010 18th European Signal Processing Conference
Date of Conference: 23-27 August 2010
Date Added to IEEE Xplore: 30 April 2015
Print ISSN: 2219-5491
Conference Location: Aalborg, Denmark