Current correlation technology revolves around likelihood ratio theory and the assignment algorithm to resolve association ambiguities. While a Bayes classifier is the best classifier, all classifiers potentially lead to classification errors. In this paper, we examine the track association and sensor registration problem in terms of several correlation classifiers, the most famous of these being the matched filter. Thus, we seek some unification between the term correlation with regards to track association and correlation with regards to pattern recognition. We examine several classes of correlation classifiers and discuss their application to the generation of a SIP when coupled with a sensor registration algorithm. The availability of these techniques on optical processing platforms is an obvious benefit to track association. We briefly discuss the implementation of some of these techniques on a commercial frequency plane correlator.
Filter construction for topological track association and sensor registration
2002
17 Seiten, 12 Quellen
Conference paper
English
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