On multitarget jump-Markov filters

Author

Mahler, Ronald

Author_Institution

Unified Data Fusion Sci., Inc., Eagan, MN, USA

fYear

2012

fDate

9-12 July 2012

Firstpage

149

Lastpage

156

Abstract

Multiple motion model (MMM) filters are a well-known approach for addressing rapidly maneuvering, noncooperative targets. Jump-Markov models provide the most well-known theoretical foundation for MMM filters. This paper addresses the problem of how to correctly generalize jump-Markov models to multitarget systems. Given this generalization, the jump-Markov version of the multisensor-multitarget Bayes filter is introduced. Then CPHD filter and PHD filter approximations of the jump-Markov multitarget Bayes filter are derived and compared with previous approaches.

Keywords

Markov processes; approximation theory; filtering theory; sensor fusion; CPHD filter; MMM filters; PHD filter approximations; generalize jump-Markov models; jump-Markov multitarget Bayes filter; multiple motion model filter; multisensor-multitarget Bayes filter; multitarget jump-Markov filters; multitarget systems; noncooperative targets; Equations; Filtering algorithms; Filtering theory; Markov processes; Mathematical model; Nonlinear systems; Probability distribution; CPHD filter; finite-set statistics; jump-Markov; multiple model; multitarget Bayes filter; random sets;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Fusion (FUSION), 2012 15th International Conference on

Conference_Location

Singapore

Print_ISBN

978-1-4673-0417-7

Electronic_ISBN

978-0-9824438-4-2

Type

conf

Filename

6289799