
For FullText PDF, please login, if you are a member of IEICE,
or go to Pay Per View on menu list, if you are a nonmember of IEICE.

ClosedForm Approximations for Gaussian Sum Smoother with Nonlinear Model
Haiming DU Jinfeng CHEN Huadong WANG
Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Vol.E99A
No.3
pp.691701 Publication Date: 2016/03/01 Online ISSN: 17451337
DOI: 10.1587/transfun.E99.A.691 Type of Manuscript: PAPER Category: Digital Signal Processing Keyword: closedform approximation, Gaussian sum smoother, nonlinear systems, Bernoulli model, probability hypothesis density (PHD), target tracking,
Full Text: PDF(2MB)>>
Summary:
Research into closedform 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 closedform solution of linear Gaussian sum smoother to nonlinear model, two closedform 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.

