DocumentCode :
1510978
Title :
Improved surrogate data tests for sea clutter
Author :
Unsworth, C.P. ; Cowper, M.R. ; Mulgrew, B. ; McLaughlin, S.
Author_Institution :
Dept. of Electron. & Electr. Eng., Edinburgh Univ., UK
Volume :
148
Issue :
3
fYear :
2001
fDate :
6/1/2001 12:00:00 AM
Firstpage :
112
Lastpage :
118
Abstract :
The first part of the paper is a comparison between the two versions of Tough and Ward´s (T/W) model (Ward and Tough 1994; and Tough and Ward 1999) in the generation of surrogate data to a compound stochastic k-distribution prescription. An overview of both versions of the model is presented together with predictor results which allow for direct comparison of the models. The second part of the paper describes the concept of surrogate data testing and introduces a new surrogate data test. The aim is to provide a new statistical hypothesis test which employs the method of surrogate data specifically for sea clutter. The test provides one with a significance measure of how appropriate the k-distribution model of Tough and Ward is for a particular sea clutter data set that is under test. This test incorporates the T/W version 2 model for the generation of its surrogate data. 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 two sea clutter data sets
Keywords :
mean square error methods; ocean waves; prediction theory; radar clutter; statistical analysis; stochastic processes; Tough and Ward model; compound stochastic k-distribution prescription; normalised mean square error; null hypothesis; predictor results; sea clutter; statistical hypothesis test; surrogate data tests; time-series; version 2 model;
fLanguage :
English
Journal_Title :
Radar, Sonar and Navigation, IEE Proceedings -
Publisher :
iet
ISSN :
1350-2395
Type :
jour
DOI :
10.1049/ip-rsn:20010281
Filename :
934999
Link To Document :
بازگشت