Paper Info
Title
SEA clutter & CHAOS: Improved surrogate-data tests
Abstract
Currently there is contention as to the nature of sea clutter for high resolution radar. Conventionally, sea clutter has been modelled as a compound stochastic k-distribution, originally suggested by Ward et al. However, recent work by Haykin et al. has suggested that the clutter can be modelled as a nonlinear deterministic process, otherwise referred to as a chaotic process. The paper presented here uses a new surrogate test which is designed specifically for this problem. The test is designed with a null hypothesis (H0= The data can be approximated to by a compound stochastic k-distribution). Therefore, acceptance of such a test will accept the conventional k-distribution as a viable model sea clutter. In addition, a new surrogate statistic is introduced which is used to reject/accept the null hypothesis. This statistic is the normalised mean square error(NMSE) from a predictor and is a statistic which can be applied to any type of time-series. An overview of the method is presented together with results for a number a sea clutter data sets.
Year
DOI
Venue
2000
10.5281/zenodo.37575
EUSIPCO
Keywords
Field
DocType
chaos,mean square error methods,radar clutter,radar resolution,statistical distributions,statistical testing,stochastic processes,nmse,chaotic process,compound stochastic k-distribution,high resolution radar,improved surrogate-data tests,nonlinear deterministic process,normalised mean square error,null hypothesis,sea clutter data sets,time-series,white noise,clutter,speckle
Statistic,Null hypothesis,Clutter,Algorithm,Mean squared error,White noise,Deterministic system,Constant false alarm rate,Statistics,Surrogate data,Mathematics
Conference
ISBN
Citations 
PageRank 
978-952-1504-43-3
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Bernard Mulgrew163989.00
Charles P Unsworth2147.47
Mark R. Cowper331.31
Stephen McLaughlin416816.62