@inproceedings{05c9d63f11814313ba01cea0a2a61b62,
title = "Gaussian mixture PHD smoother for jump Markov models in multiple maneuvering targets tracking",
abstract = "This paper presents a Gaussian mixture probability hypothesis density (GM-PHD) smoother for tracking multiple maneuvering targets that follow jump Markov models. Unlike the generalization of the multiple model GM-PHD filters, our aim is to approximate the dynamics of the linear Gaussian jump Markov system (LGJMS) by a best-fitting Gaussian (BFG) distribution so that the GM-PHD smoother can be carried out with respect to an approximated linear Gaussian system. Our approach is inspired by the recognition that the BFG approximation provides an accurate performance measure for the LGJMS. Furthermore, the multiple model estimation is avoided and less computational cost is required. The effectiveness of the proposed smoother is verified with a numerical simulation.",
author = "Wenling Li and Yingmin Jia and Junping Du and Fashan Yu",
year = "2011",
doi = "10.1109/ACC.2011.5991161",
language = "英语",
isbn = "9781457700804",
series = "Proceedings of the American Control Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3024--3029",
booktitle = "Proceedings of the 2011 American Control Conference, ACC 2011",
address = "美国",
}