Title

Statistical And Neural Techniques For Processing Of Nonparametric Geophysical Mine Data

Keywords

Artificial neural networks; Geophysical signal processing; Mine detection

Abstract

This paper analyzes the effectiveness of combining certain statistical techniques with a neural network to improve land mine 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 simple statistical techniques such as the energy detection method and the stand-alone statistical techniques. Our results show that the combination of these techniques with a neural network improves performance over these alone.

Publication Date

12-1-2005

Publication Title

13th European Signal Processing Conference, EUSIPCO 2005

Number of Pages

57-60

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

84863707258 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84863707258

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