Name
Abstract | ||
|---|---|---|
This paper deals with covariance matrix estimates in impulsive noise environments. Physical models based on compound noise modeling [spherically invariant random vectors (SIRV), compound Gaussian processes] allow to correctly describe reality (e.g., range power variations or clutter transitions areas in radar problems). However, these models depend on several unknown parameters (covariance matrix, statistical distribution of the texture, disturbance parameters) that have to be estimated. Based on these noise models, this paper presents a complete analysis of the main covariance matrix estimates used in the literature. Four estimates are studied: the well-known sample covariance matrix MSCM and a normalized version MN, the fixed-point (FP) estimate MFP, and a theoretical benchmark MTFP. Among these estimates, the only one of practical interest in impulsive noise is the FP. The three others, which could be used in a Gaussian context, are, in this paper, only of academic interest, i.e., for comparison with the FP. A statistical study of these estimates is performed through bias analysis, consistency, and asymptotic distribution. This study allows to compare the performance of the estimates and to establish simple relationships between them. Finally, theoretical results are emphasized by several simulations corresponding to real situations. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/TSP.2007.914311 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
well-known sample covariance matrix,gaussian context,paper deal,compound noise modeling,covariance matrix estimates,noise model,covariance matrix,impulsive noise,covariance matrix estimate,impulsive noise environment,performance analysis,main covariance matrix,physical model,clutter,bias,asymptotic distribution,statistical distribution,fixed point,estimation theory,interference,stochastic processes,impulse noise,consistency,gaussian processes,signal processing,vectors,gaussian noise,gaussian process | Applied mathematics,Mathematical optimization,Covariance function,Estimation of covariance matrices,Multivariate random variable,Gaussian process,Covariance matrix,Statistics,Gaussian noise,Scatter matrix,Mathematics,Covariance | Journal |
Volume | Issue | ISSN |
56 | 6 | 1053-587X |
Citations | PageRank | References |
27 | 1.40 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| F. Pascal | 1 | 102 | 6.27 |
| P. Forster | 2 | 187 | 16.94 |
| Jean Philippe Ovarlez | 3 | 190 | 25.11 |
| Pascal Larzabal | 4 | 535 | 64.76 |