A back-propagation neural network landmine detector using the delta-technique and S-statistic

    Authors

    T. Kocak;M. Draper

    Comments

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    Abbreviated Journal Title

    Neural Process. Lett.

    Keywords

    back-propagation neural networks; false alarm filtering; mine detection; S-statistic; delta-technique; MINE DETECTION; Computer Science, Artificial Intelligence; Neurosciences

    Abstract

    Landmines are a major problem facing the world today; there are millions of these deadly weapons still buried in various countries around the world. Humanitarian organizations dedicate an immeasurable amount of time, effort, and money to find and remove as many of these mines as possible. Unfortunately, landmines can be made out of common materials which make the correct detection of them very difficult. This paper analyzes the effectiveness of combining certain statistical techniques with a neural network to improve detection. The detection method must not only detect the majority of landmines in the ground, it must also filter out as many of the false alarms as possible. This is the true challenge to developing landmine detection algorithms. Our approach combines a Back-Propagation Neural Network (BPNN) with statistical techniques and compares the performance of mine detection against the performance of the energy detector and the delta-technique. Our results show that the combination of the delta-technique and the S-statistics with a neural network improves the performance.

    Journal Title

    Neural Processing Letters

    Volume

    23

    Issue/Number

    1

    Publication Date

    1-1-2006

    Document Type

    Article

    Language

    English

    First Page

    47

    Last Page

    54

    WOS Identifier

    WOS:000235318300003

    ISSN

    1370-4621

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