Title :
Marked Multitarget Intensity Filters
Author_Institution :
Metron, Reston, VA, USA
Abstract :
Probability Hypothesis Density and other intensity filters are based on modeling the multitarget state as a realization of a Poisson point process (PPP). Target identifiability is lost in these models; consequently, the filters require targets to have the same motion models and data likelihood functions to be the same for all targets. These are unrealistic limitations in some applications. The Marked Multitarget Intensity Filter (MMIF) presented here enables the use of heterogeneous target motion models and data likelihood functions. The MMIF uses a marked PPP target model together with a parameterized PPP intensity function. The parametric model is an affine, joint, linear-Gaussian sum on the joint measurement-target space. The “at most one measurement per target” rule is enforced in the mean.
Keywords :
filtering theory; stochastic processes; target tracking; Poisson point process; marked multitarget intensity filters; probability hypothesis density filters; target identifiability; Clutter; Covariance matrix; Data models; Joints; Kalman filters; Noise measurement; Time measurement; Expectation-Maximization; Gaussian sum filter; Heterogeneous target models; Intensity filter; Marked process; Marking Theorem; Microtargets; PHD filter; Poisson point process;
Conference_Titel :
Information Fusion (FUSION), 2010 13th Conference on
Conference_Location :
Edinburgh
Print_ISBN :
978-0-9824438-1-1
DOI :
10.1109/ICIF.2010.5711924