Use of Literal Information in Multi-Target Data Association

It has been shown that literal information can enhance geolocation information in the multi-target tracking and data association problem. This paper continues previous efforts in establishing a systematic approach to the combination of both types of information using membership functions based upon multiple-valued logic. Filters are established for literal and non-numerical attributes, somewhat analogous to the well-known Kalman filter. The major result, however, is an improvement and clarification of a previous theorem establishing asymptotic forms for the posterior possibility distribution of the unknown data association parameter as information granularity decreases and as inference rule structures become more definitive.