Multisensor Decision and Estimation Fusion

In recent years, research activity in multisensor decision and estimation fusion problems has significantly increased. Distributed decision and estimation fusion problems for cases with statistically independent observations - or observation noises - have received the most attention, while problems with statistically dependent observations have been given much less consideration. Multisensor Decision and Estimation Fusion provides a more complete treatment of the fundamentals of multisensor decision and estimation fusion in order to deal with general random observations or observation noises that are correlated across the sensors. Recent progress is presented in two ways. For multisensor decision fusion with general sensor observations given a fixed fusion rule, the book demonstrates a necessary condition for optimum sensor rules - they must be a fixed point of an integral operator related to given conditional probability densities. In particular, the book presents unified/optimal fusion rules for some specific decision systems. For the multisensor point estimation fusion problem, a general version of the linear unbiased minimum variance estimation fusion rule is developed. In addition, several new interval estimation fusion methods are proposed.Yunmin Zhu is the author of 'Multisensor Decision and Estimation Fusion' with ISBN 9781402072581 and ISBN 1402072589.