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The challenge of border security agents across the world is to detect contraband before they are smuggled into the country. To help achieve this, different sensors are used to sense for various substances. However, with high rate of false alarms and false negatives, there is need to develop a system to restore operator confidence and improve detection. This paper by simulating sample data, suggests a fusion of data from two sensors using the Bayesian Inference showing an improved and therefore producing a more reliable detection.