Correlation engines have been evolving since the implementation of radar. Here, correlation refers to association, which in this context is track-to-track association. In modern sensor fusion architectures, correlation and sensor registration are required to produce common, continuous, and unambiguous tracks of all objects in the surveillance area. The objective is to provide a unified picture of the theatre or area of interest to battlefield decision makers. This unified picture has many names, but is most commonly referred to as a Single Integrated Picture (SIP). A related process, known as sensor registration or gridlock filtering (gridlocking), refers to the reduction in navigation errors and sensor misalignment errors so that one sensor's track data can be accurately transformed into another sensor's coordinate system. As platforms gain multiple sensors, the correlation and gridlocking of tracks become significantly more difficult.
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.