ABSTRACT
This chapter introduces the application of sensor and data fusion to traffic management. It reviews the definitions of sensor and data fusion, their role in enhancing the effectiveness of traffic management strategies, and examines factors that influence the selection of a fusion architecture. The Joint Directors of Laboratories (JDL) data fusion levels are intended only as a convenient categorization of data fusion functions. According to the JDL data fusion model, data fusion is a multilevel, multifaceted process dealing with the automatic detection, correlation, and combination of data and information from single and multiple sources to achieve refined position and identity estimates, and complete and timely assessments of situations and impacts and their significance. The majority of the algorithms are concerned with data and track association techniques. Particle filters extend the sequential Monte Carlo algorithm by utilizing a weighted ensemble of randomly drawn samples called particles as an approximation of the probability density of interest.
