Landmine discrimination using wavelet decomposition and weighted density distribution functions

Author

Kalyan Ram Achanta

Abstract

"Landmine detection using hand-held units is a difficult problem due to varying type and composition of metals in landmines. This research explores spatially distributed features to discriminate landmines from other harmless objects. The feature calculation involves the wavelet decomposition of Metal Detector (MD) energy sequences to obtain the approximate and detailed coefficients, and correlation of these coefficients with Weighted Density Distribution (WDD) functions. The features calculated are evaluated on a standard back propagation neural network on real data sets with more than 1500 mine encounters of varying shape, size and metal content. The effectiveness of wavelet decomposition and WDD functions is investigated"--Abstract, page iii.

Advisor(s)

Stanley, R. Joe

Committee Member(s)

Agarwal, Sanjeev, 1971-
Moss, Randy Hays, 1953-

Department(s)

Electrical and Computer Engineering

Degree Name

M.S. in Electrical Engineering

Publisher

University of Missouri--Rolla

Publication Date

Summer 2004

Pagination

viii, 50 pages

Note about bibliography

Includes bibliographical references (pages 48-49).

Rights

© 2004 Kalyan Ram Achanta, All rights reserved.

Document Type

Thesis - Restricted Access

File Type

text

Language

English

Subject Headings

Land mines -- Detection
Mines (Military explosives) -- Detection
Wavelets (Mathematics)

Thesis Number

T 8623

Print OCLC #

62231101

Link to Catalog Record

Electronic access to the full-text of this document is restricted to Missouri S&T users. Otherwise, request this publication directly from Missouri S&T Library or contact your local library.

Recommended Citation

Achanta, Kalyan Ram, "Landmine discrimination using wavelet decomposition and weighted density distribution functions" (2004). Masters Theses. 3621.
https://scholarsmine.mst.edu/masters_theses/3621