Publica
Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Accumulated State Densities and Their Applications in Object Tracking
 Fourati, H.: Multisensor data fusion : From algorithm and architecture design to applications Boca Raton, Fla.: CRC Press, 2017 ISBN: 9781482263756 pp.295329 

 English 
 Book Article 
 Fraunhofer FKIE () 
Abstract
Accumulated state density (ASD) provides a unified treatment of filtering and retrodiction insofar as by marginalizing them appropriately, the standard filtering and retrodiction densities are obtained. ASDs are useful in tracking applications, where outofsequence (OoS) measurements are to be processed, that is, when sensor data do not arrive in temporal order, in which they have been produced. This chapter discusses why ASDs provide an exact solution to the tracktotrack fusion (T2TF) problem. It summarizes basic facts on the Bayesian tracking paradigm. The chapter presents the notion of an ASD along with a discussion of closedformulae for the parameters of the ASD in the case, where Kalman filtering can be applied to tracking. It also discusses the role of ADS within the probabilistic multiple hypothesis tracking (PMHT) framework. A Bayesian tracking algorithm is an iterative updating scheme for calculating conditional probability density functions p(xlZk) that represent all available knowledge on the object states xl at discrete instants of time tl.