Masters Theses


"Land mine detection using metal detector (MD) and ground penetrating radar (GPR) sensors in hand-held units is a difficult problem. Detection difficulties arise for several reasons, including the varying composition and type of metal in land mines and the time-varying nature of backgrounds, among others. This research explores spatially distributed MD features for differentiating land mine signatures from backgrounds. The spatially distributed features involve correlating sequences of MD energy values with six weighted density distribution functions. These features are evaluated using a standard back propagation neural network on real data sets containing more than 2,300 mine encounters of different size, shape, content and metal composition that are measured under different soil conditions. The effectiveness of these features to detect landmines of varying metal composition and type is investigated. Also, experimental results are presented from statistical analysis for feature assessment"--Abstract, page iii.


Stanley, R. Joe

Committee Member(s)

Miller, Ann K.
Dagli, Cihan H., 1949-


Electrical and Computer Engineering

Degree Name

M.S. in Electrical Engineering


University of Missouri--Rolla

Publication Date

Summer 2002


viii, 38 pages

Note about bibliography

Includes bibliographical references (pages 36-37).


© 2002 Satish Chandra Somanchi, All rights reserved.

Document Type

Thesis - Restricted Access

File Type




Subject Headings

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

Thesis Number

T 8103

Print OCLC #


Link to Catalog Record

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