Shafer-Dempster and Bayesian reasoning: a response to `Shafer-Dempster reasoning with applications to multisensor target identification systems´

In a previously published paper (ibid., vol.17, no.6, p.968-77, 1987) P.L. Bogler made four points that require further clarification and/or correction concerning Bayesian probabilistic reasoning for the multisensor fusion of identification data: 1) the Bayesian approach forces a common level of abstraction to be defined for all sensors, a level of abstraction that is not meaningful for some sensors; 2) Bayesian results can be unstable and intuitively unsatisfying; 3) Bayesian results are not commutative; and 4) Bayesian results for friend/foe identification can force false inferences concerning the identification of specific aircraft types. These assertions are reviewed and shown to be incorrect. In addition, it is shown that all of the examples of Dempster´s rule of combination are identical to Bayesian probability theory. The contention here is not that the Shafer-Dempster approach to uncertainty management is identical to Bayesian probability theory, but rather that the Shafer-Dempster approach is not fully illustrated