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: Joint Tracking of Manoeuvring Targets and Classification of Their Manoeuvrability | EURASIP Journal on Applied Signal Processing 2004 15 2339-2350 2004 Hindawi Publishing Corporation Joint Tracking of Manoeuvring Targets and Classification of Their Manoeuvrability Simon Maskell QinetiQ Ltd St. Andrews Road Malvern Worcestershire WR14 3PS UK Email smaskell@ Department of Engineering University of Cambridge Cambridge CB2 1PZ UK Received 30 May 2003 Revised 23 January 2004 Semi-Markov models are a generalisation of Markov models that explicitly model the state-dependent sojourn time distribution the time for which the system remains in a given state. Markov models result in an exponentially distributed sojourn time while semi-Markov models make it possible to define the distribution explicitly. Such models can be used to describe the behaviour of manoeuvring targets and particle filtering can then facilitate tracking. An architecture is proposed that enables particle filters to be both robust and efficient when conducting joint tracking and classification. It is demonstrated that this approach can be used to classify targets on the basis of their manoeuvrability. Keywords and phrases tracking classification manoeuvring targets particle filtering. 1. INTRODUCTION When tracking a manoeuvring target one needs models that can cater for each of the different regimes that can govern the target s evolution. The transitions between these regimes are often either explicitly or implicitly taken to evolve according to a Markov model. At each time epoch there is a probability of being in one discrete state given that the system was in another discrete state. Such Markov switching models result in an exponentially distributed sojourn time the time for which the system remains in a given discrete state. SemiMarkov models also known as renewal processes 1 are a generalisation of Markov models that explicitly model the discrete-state-dependent distribution over sojourn time. At each time epoch there is a probability of being in one discrete state .
