Data fusion and evidence accumulation for landmine detection using Dempster-Shafer algorithm

Karthik Ramaswamy, Sanjeev Agarwal, Vittal Rao

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

In this paper, an architecture for multi-sensor data fusion and evidence accumulation for landmine detection and discrimination is presented. Evidential and discriminatory information about the buried object such as shape, size, depth, and material, chemical or electromagnetic properties is obtained from different sensors and sensor algorithms. A streamlined assimilation of these varied information from dissimilar and non-homogeneous sensors and sensor algorithms is presented. Information theory based pre-processing of the data and subsequent unsupervised clustering using Dignet architecture is used to capture the underlying structure of the information available from different sensors. Sensor information is categorized into type, size, depth, and position data channels. Each sensor may provide one or more of this information. Type data channel provides any relevant discriminatory characteristics of the buried object. A supervised feed-forward neural network is used to learn the causality between the cluster information and the evidence of a given class of the buried object. Size, depth and phenomenology input are used as control gating input for the neural network mapping. The supervisory feedback is provided by the output of the global sensor fusion system and accommodates both autonomous (adaptive) and human assisted learning. Dempster-Shafer evidential reasoning is used to accumulate different evidence from sensor channels and thus to detect and discriminate between different types of buried landmine and clutter. Performance of fusion architecture and Dempster-Shafer reasoning is studied using simulated data. For the simulated data noisy images of regular and irregular shapes of different objects are produced. Fourier descriptor, moment invariant and Matlab shape features are used to define the shape information of the objects. Evidence accumulation is done using shape and size information from each of the algorithms.

Original languageEnglish
Pages (from-to)II/-
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume4038
StatePublished - 2000
EventDetection and Remediation Technologies for Mines and Minelike Targets V - Orlando, FL, USA
Duration: Apr 24 2000Apr 28 2000

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