IEICE TRANSACTIONS on Fundamentals
Closed-Form Approximations for Gaussian Sum Smoother with Nonlinear Model
Summary :
Research into closed-form Gaussian sum smoother has provided an attractive approach for tracking in clutter, joint detection and tracking (in clutter), and multiple target tracking (in clutter) via the probability hypothesis density (PHD). However, Gaussian sum smoother with nonlinear target model has particular nonlinear expressions in the backward smoothed density that are different from the other filters and smoothers. In order to extend the closed-form solution of linear Gaussian sum smoother to nonlinear model, two closed-form approximations for nonlinear Gaussian sum smoother are proposed, which use Gaussian particle approximation and unscented transformation approximation, separately. Since the estimated target number of PHD smoother is not stable, a heuristic approximation method is added. At last, the Bernoulli smoother and PHD smoother are simulated using Gaussian particle approximation and unscented transformation approximation, and simulation results show that the two proposed algorithms can obtain smoothed tracks with nonlinear models, and have better performance than filter.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E99-A No.3 pp.691-701
- Publication Date
- 2016/03/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E99.A.691
- Type of Manuscript
- PAPER
- Category
- Digital Signal Processing
Authors
Haiming DU
Zhengzhou University of Light Industry
Jinfeng CHEN
Cleveland State University
Huadong WANG
Chongqing University of Posts and Telecommunications
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Haiming DU, Jinfeng CHEN, Huadong WANG, "Closed-Form Approximations for Gaussian Sum Smoother with Nonlinear Model" in IEICE TRANSACTIONS on Fundamentals,
vol. E99-A, no. 3, pp. 691-701, March 2016, doi: 10.1587/transfun.E99.A.691.
Abstract: Research into closed-form Gaussian sum smoother has provided an attractive approach for tracking in clutter, joint detection and tracking (in clutter), and multiple target tracking (in clutter) via the probability hypothesis density (PHD). However, Gaussian sum smoother with nonlinear target model has particular nonlinear expressions in the backward smoothed density that are different from the other filters and smoothers. In order to extend the closed-form solution of linear Gaussian sum smoother to nonlinear model, two closed-form approximations for nonlinear Gaussian sum smoother are proposed, which use Gaussian particle approximation and unscented transformation approximation, separately. Since the estimated target number of PHD smoother is not stable, a heuristic approximation method is added. At last, the Bernoulli smoother and PHD smoother are simulated using Gaussian particle approximation and unscented transformation approximation, and simulation results show that the two proposed algorithms can obtain smoothed tracks with nonlinear models, and have better performance than filter.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E99.A.691/_p
Copy
@ARTICLE{e99-a_3_691,
author={Haiming DU, Jinfeng CHEN, Huadong WANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Closed-Form Approximations for Gaussian Sum Smoother with Nonlinear Model},
year={2016},
volume={E99-A},
number={3},
pages={691-701},
abstract={Research into closed-form Gaussian sum smoother has provided an attractive approach for tracking in clutter, joint detection and tracking (in clutter), and multiple target tracking (in clutter) via the probability hypothesis density (PHD). However, Gaussian sum smoother with nonlinear target model has particular nonlinear expressions in the backward smoothed density that are different from the other filters and smoothers. In order to extend the closed-form solution of linear Gaussian sum smoother to nonlinear model, two closed-form approximations for nonlinear Gaussian sum smoother are proposed, which use Gaussian particle approximation and unscented transformation approximation, separately. Since the estimated target number of PHD smoother is not stable, a heuristic approximation method is added. At last, the Bernoulli smoother and PHD smoother are simulated using Gaussian particle approximation and unscented transformation approximation, and simulation results show that the two proposed algorithms can obtain smoothed tracks with nonlinear models, and have better performance than filter.},
keywords={},
doi={10.1587/transfun.E99.A.691},
ISSN={1745-1337},
month={March},}
Copy
TY - JOUR
TI - Closed-Form Approximations for Gaussian Sum Smoother with Nonlinear Model
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 691
EP - 701
AU - Haiming DU
AU - Jinfeng CHEN
AU - Huadong WANG
PY - 2016
DO - 10.1587/transfun.E99.A.691
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E99-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2016
AB - Research into closed-form Gaussian sum smoother has provided an attractive approach for tracking in clutter, joint detection and tracking (in clutter), and multiple target tracking (in clutter) via the probability hypothesis density (PHD). However, Gaussian sum smoother with nonlinear target model has particular nonlinear expressions in the backward smoothed density that are different from the other filters and smoothers. In order to extend the closed-form solution of linear Gaussian sum smoother to nonlinear model, two closed-form approximations for nonlinear Gaussian sum smoother are proposed, which use Gaussian particle approximation and unscented transformation approximation, separately. Since the estimated target number of PHD smoother is not stable, a heuristic approximation method is added. At last, the Bernoulli smoother and PHD smoother are simulated using Gaussian particle approximation and unscented transformation approximation, and simulation results show that the two proposed algorithms can obtain smoothed tracks with nonlinear models, and have better performance than filter.
ER -
English
