Landmine discrimination via Bayesian adaptive multimodal processing: results for handheld and vehicular sensors

Yongli Yu; Peter A. Torrione; Leslie M. Collins

doi:10.1117/12.542276

21 September 2004 Landmine discrimination via Bayesian adaptive multimodal processing: results for handheld and vehicular sensors

Yongli Yu, Peter A. Torrione, Leslie M. Collins

Author Affiliations +

Proceedings Volume 5415, Detection and Remediation Technologies for Mines and Minelike Targets IX; (2004) https://doi.org/10.1117/12.542276
Event: Defense and Security, 2004, Orlando, Florida, United States

Abstract

The recent development of high quality sensors paired with development of advanced statistical signal processing algorithms has shown that there are sensors that can not only discriminate targets from clutter, but can also identify subsurface or obscured targets. In a previous theoretical and simulation study, we utilized this identification capability in addition to contextual information in a multi-modal adaptive algorithm where the identification capabilities of multiple sensors are utilized to modify the prior probability density functions associated with statistical models being utilized by other sensors. We assumed that the statistics describing the features associated with each sensor modality follow a Gaussian mixture density, where in many cases the individual Gaussian distributions that make up the mixture result from different target types or target classes. We utilized identification information from one sensor to modify the weights associated with the probability density functions being utilized by algorithms associated with other sensor modalities. In our simulations, this approach is shown to be improve sensor performance by reducing the overall false alarm rate. In this talk, we transition the approach from a simulation study to consider real field data collected by both handheld and vehicular based systems. We show that by appropriate modification of our statistical models to accurately match field data, improved performance can be obtained over traditional sensor fusion algorithms.

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Yongli Yu, Peter A. Torrione, and Leslie M. Collins "Landmine discrimination via Bayesian adaptive multimodal processing: results for handheld and vehicular sensors", Proc. SPIE 5415, Detection and Remediation Technologies for Mines and Minelike Targets IX, (21 September 2004); https://doi.org/10.1117/12.542276

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KEYWORDS

Sensors

General packet radio service

Metals

Detection and tracking algorithms

Electromagnetic coupling

Land mines

Mining

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