Data Fusion Algorithms and Architectures

Abstract

Sensor and data fusion are used in applications ranging from Earth resource monitoring, weather forecasting, and vehicular traffic control to military target classification and tracking. Data fusion and its objectives based on a model developed for the U.S. Department of Defense are discussed in this chapter. The model divides data fusion into low-level and high-level processes. The low-level processes support preprocessing of data; target detection, classification, and identification; and target tracking. High-level processes support situation and threat assessment and fusion process refinement. Various classes of algorithms have been developed to implement target detection, classification, and track estimation fusion. In addition, several types of data fusion architectures exist for combining sensor data to support the requirements of the data fusion model. The architectures are differentiated by the amount of processing applied to the sensor data before transmission to the fusion process, resolution of the data that are combined, and the location of the data fusion process. The chapter concludes by addressing several concerns associated with the registration of multisensor data. These issues encompass dissimilar sensor footprint sizes, signal generation phenomena, and uncertainty in the location of the sensors. 3.1 Definition of data fusion In an effort to encourage the use of sensor and data fusion to enhance (1) target detection, classification, identification, and tracking and (2) situation and threat assessment in real time with affordable, survivable, and maintainable systems, the Assistant Secretary of Defense for C3I (Command, Control, Communications, and Integration) empowered the Joint Directors of Laboratories Data Fusion Subpanel (JDL DFS), now called the Data Fusion Group, to codify data fusion terminology and improve the efficiency of data fusion programs through the exchange of technical information. Acting on this directive, the Office of Naval Technology (ONT) chartered a group, the Data Fusion Development Strategy (DFDS) Panel, to devise a plan for guiding future ONT investment in data fusion. The results of their activity form the basis for the objectives and functional description of data fusion presented here.