The system probability information content (PIC) relationship to contributing components, combining independent multi-source beliefs, hybrid and pedigree pignistic probabilities

In the design of information fusion systems, the reduction of computational complexity is a key design parameter for real-time implementations. One way to simplify the computations is to decompose the system into subsystems of non-correlated informational components, such as a qualitative informational component, a quantitative informational component, and a complement informational component. A probability information content (PIC) variable (Sudano, 2001) assigns an information content value to any set of system or sub-system probability distributions. The PIC variable is the normalized entropy computed from the probability distribution. This article derives a PIC variable for a subsystem represented by the complement probabilities. This article also derives a relationship between the PIC variable of sub-system components and the system informational PIC variable. A hybrid pignistic probability is introduced that is robust in estimating a probability for any maturity of the incomplete data set. A new methodology of combining independent multisource beliefs is presented. A pedigree pignistic probability is introduced that uses some information of the original fused data sets to compute a better pignistic probability.