DocumentCode
2371841
Title
A Bayesian algorithm for object detection in GPR data
Author
Angelova, Donka
Author_Institution
Inst. for Parallel Process., Bulgarian Acad. of Sci., Sofia
fYear
2008
fDate
21-23 May 2008
Firstpage
1
Lastpage
4
Abstract
An algorithm for underground object detection in GPR data is presented in this paper. It combines the advantages of Kalman filtering approach, suggested by Carevic (1999) with the robustness of hybrid Bayesian estimation technique. The objective is to increase the reliability of target detection and target-background separation while keeping a small false alarm rate. In addition, sequential change detection (CUSUM) test is studied for the purposes of ground layers segmentation and object recognition.
Keywords
Bayes methods; Kalman filters; ground penetrating radar; object detection; radar target recognition; Bayesian estimation technique; GPR data; Kalman filtering; ground layer segmentation; ground penetrating radar; object recognition; sequential change detection; target detection; target-background separation; underground object detection; Bayesian methods; Filtering; Ground penetrating radar; Kalman filters; Object detection; Parameter estimation; Robustness; Sequential analysis; State estimation; Technological innovation;
fLanguage
English
Publisher
ieee
Conference_Titel
Radar Symposium, 2008 International
Conference_Location
Wroclaw
Print_ISBN
978-83-7207-757-8
Type
conf
DOI
10.1109/IRS.2008.4585776
Filename
4585776
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