Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article On Sequential Track Extraction within the PMHT Framework | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 276914 13 pages doi 2008 276914 Research Article On Sequential Track Extraction within the PMHT Framework Monika Wieneke and Wolfgang Koch FGAN-FKIE Neuenahrer Strasse 20 53343 Wachtberg Germany Correspondence should be addressed to Monika Wieneke wieneke@ Received 1 April 2007 Revised 17 August 2007 Accepted 8 October 2007 Recommended by T. Luginbuhl Tracking multiple targets in a cluttered environment is a challenging task. Probabilistic multiple hypothesis tracking PMHT is an efficient approach for dealing with it. Essentially PMHT is based on expectation-maximization for handling with association conflicts. Linearity in the number of targets and measurements is the main motivation for a further development and extension of this methodology. In particular the problem of track extraction and deletion is apparently not yet satisfactorily solved within this framework. A sequential likelihood-ratio LR test for track extraction has been developed and integrated into the framework of traditional Bayesian multiple hypothesis tracking by Gunter van Keuk in 1998. As PMHT is a multiscan approach as well it also has the potential for track extraction. In this paper an analogous integration of a sequential LR test into the PMHT framework is proposed. We present an LR formula for track extraction and deletion using the PMHT update formulae. The LR is thus a by-product of the PMHT iteration process as PMHT provides all required ingredients for a sequential LR calculation. Therefore the resulting update formula for the sequential LR test affords the development of track-before-detect algorithms for PMHT. The approach is illustrated by a simple example. Copyright 2008 M. Wieneke and W. Koch. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided
