An obvious use for feature and attribute data is for classification and in combat identification. The term classification is used broadly here to include discrimination, detection, target typing, identification, and pattern recognition. An additional use is in the data (or track) association process to reduce the misassociations, often called feature aided tracking. Previous papers discussed the integration of features and attributes into target track processing in addition to their use in multiple target classification. The distinction is made between feature and attribute data because they are processed differently. The term features applies to random variables from continuous sample space and attributes applies to random variable data from discrete sample space.
The primary concern of this paper is to address processing of attributes with data from legacy sensors. For example, in fusion of sensor data from distributed sensors, a sensor might distribute the likelihood of the most likely target class (or attribute property) and no information on the other possibilities. In another example, a sensor might distribute the likelihood that the target is any one of a number of target classes, a subset of all the target classes, rather than indicating one specific target class. Both attribute aided tracking and (post tracker) target classification are addressed for these examples of incomplete data from legacy sensors. The purpose of this paper is to show the feasibility of simple approaches to dealing with data from some legacy sensors.