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Mathematical aspects of Data Fusion in Multiple Search and Tracking Systems
Sensors with different behaviors and technologies collect multiple diverse aspects of the reality. Working together, they can provide a deep analysis of the operational scenario and increase situational awareness. But diversity means also different outputs in terms of quantities, resolution, reliability, consistency, extension and range. The information collected by the different sensors could be fully redundant or, simply and more commonly, qualitatively redundant, it can include measurements of the same quantities, but with different accuracy and bandordifferent measurements at all.That could sometimes make hard, or simply not cost effective, a profitable fusion of data. The chapter will attempt to analyze that kind of problems and provides methods to cope with them. The detailed analysis is limited to the fusion of data from Search and Track Systems,whose the most common are the RADARand the IRST, but the proposed mathematics can be also extended to many other different situations more general and broad. In fusion of Search and Track Systems, one of the key aspects is the association of detections or tracks to the same physical object. Especially in an operational scenario, the threats could be close to one another, or distributed in such a way to provide less evidence of the level of their effective dangerousness. A precise association of the detections or tracks from different sensors is, in that case, of paramount importance for the success of the fusion process and the success of the mission. After a description in general terms and the exposition of the mathematical approach, the analysis is carried out considering the fusion of data between RADAR and IRST. The two sensors allow to expose in a clear way the method of data fusion in case of heterogeneous measurements, since theRADAR provides an accurate range but a limited accuracy in angles, while the IRST performs an approximate evaluation of the rangeor, as often it happens, it does not provide any range information at all, but keeps a precise angular position of tracks. For reducing the probability of bad associations between close tracks, the Joint Probabilistic Data Association (JPDA) is tailored specifically for data fusion. To improvethe tracking process of the fused tracks in the presence of fast variation,specific fusion equationsare suggested and described.
Quaranta Carlo, Balzarotti Giorgio
Paper for Book
Chapter included in "Data Fusion: Methods, Applications and Research", Nova Science Publishers, Inc (New York, NY, USA)
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