Presents the only complete reference for the Probabilistic Multi-Hypothesis Tracker (PMHT)-algorithm

Offers an accessible introduction to the method, including an intuitive derivation

Provides application examples

Autorentext

Samuel Davey studied engineering and mathematics at the University of Adelaide, culminating with a Ph.D. in signal processing in 2003. He has worked for the Defence Science and Technology Group, Australia, since 1995 in the areas of target tracking, tracker performance assessment, and multi-sensor

fusion; he is currently Group Leader, Geophysical Phenomenology and Performance Assessment. He is also a Visiting Research Fellow at the University of Adelaide, a senior member of the IEEE, and an associate editor of IEEE Signal Processing Letters. He is a co-author of the book Bayesian Methods in the Search for MH370. He received the 2011 JP LeCadre award for the best paper at the International Conference on Information Fusion and the 2012 DST Science and Engineering Excellence award for work on H-PMHT that led to this book.

Han Gaetjens received an Honours degree in Mathematics from the University of South Australia in 2007 and the Ph.D. from the University of Adelaide in 2015. She has worked for the Defence Science and Technology Group, Australia, since 2007.

Klappentext

This book offers a detailed description of the histogram probabilistic multi-hypothesis tracker (H-PMHT), providing an accessible and intuitive introduction to the mathematical mechanics of H-PMHT as well as a definitive reference source for the existing literature on the method. Beginning with basic concepts, the authors then move on to address extensions of the method to a broad class of tracking problems. The latter chapters present applications using recorded data from experimental radar, sonar and video sensor systems. The book is supplemented with software that both furthers readers' understanding and acts as a toolkit for those who wish to apply the methods to their own problems.

Inhalt

Foreword.- Introduction.- Idealised Track-Before-Detect.- Point Measurement Probabilistic Multi-Hypothesis Tracking.- Histogram ProbabilisticMulti-Hypothesis Tracking.- Implementation Considerations.- Poisson Scattering Field.- Non-linear Non-Gaussian Targets.- Adaptive Appearance Models.- H-PMHT with Attribute Data.- Radar and Sonar Track-Before-Detect.- Tracking in Full Motion Video.- Related Methods.- Summary and Prognostications.