SPIE - Education Multisensor Data Fusion for Object Detection, Classification and Identification SC994

This course describes sensor and data fusion methods that improve the probability of correct target detection, classification, and identification. The methods allow the combining of information from collocated or dispersed sensors that utilize similar or different operating phenomenologies. Examples provide insight as to how different phenomenology-based sensors enhance a data fusion system. After introducing the JDL data fusion model, sensor and data fusion architectures are described in terms of sensor-level, central-level, and hybrid fusion, and pixel-, feature-, and decision-level fusion. The exploration of data fusion algorithm taxonomies provides an introduction to the algorithms and methods utilized for detection, classification, identification, and state estimation and tracking – the Level 1 fusion processes. These algorithms support the higher-level data fusion processes of situation and impact assessment. Subsequent sections of the course more fully develop the Bayesian, Dempster-Shafer, and voting logic data fusion algorithms. Examples abound throughout the material to illustrate the major techniques being presented. The illustrative problems demonstrate that many of the data fusion methods can be applied to combine information from almost any grouping of sensors as long as the input data are of the types required by the fusion algorithm.