Shafer-dempster reasoning with applications to multisensor target identification systems

Bayes theory is provably optimal whenever all the sensor sources are contributing information at a single Bayesian level of abstraction. Often, however, this is not the case. Shafer-Dempster reasoning is a generalization of Bayes reasoning that offers a way to combine uncertain information from disparate sensor sources with different levels of abstraction. Shafer-Dempster logic is discussed and described, and realistic examples are provided of its use drawn from the field of multisensor target identification systems and on simulating its operation.