Multi Sensor Data Fusion and Smart Decision Making Using Dempster-Shafer Theory: A Case Study

Parallel distributed detection system consists of several separate sensor-detector nodes (separated spatially or by their principles of operation), each with some processing capabilities. These local sensor-detectors send some information on an observed phenomenon to a centrally located Data Fusion Center for aggregation and decision making. Several techniques are developed to combine data from sensor ? detector nodes. This article focuses on heterogeneous sensor data fusion using Dempster-Shafer evidence theory, which is one of the most effective approaches for sensor data fusion. The Dempster ? Shafer theory of evidence has uncertainty management and inference mechanisms analogous to our human reasoning process. This paper describes the use of Dempster- Shafer theory for multi sensor data fusion and demonstrates the easiness of using Dempster Shafer engine for obtaining inference through a simple case study.