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A Gaussian Mixture PHD Filter for Jump Markov System Models
2009
IEEE Transactions on Aerospace and Electronic Systems
The probability hypothesis density (PHD) filter is an attractive approach to tracking an unknown and time-varying number of targets in the presence of data association uncertainty, clutter, noise, and detection uncertainty. The PHD filter admits a closed form solution for a linear Gaussian multi-target model. However, this model is not general enough to accommodate maneuvering targets that switch between several models. In this paper, we generalize the notion of linear jump Markov systems to
doi:10.1109/taes.2009.5259174
fatcat:cvxyf4qdevff3a6mukewsiw5ke