Merging-based forward-backward smoothing on Gaussian mixtures

被引：0

作者：

Rahmathullah, Abu Sajana ^{[1
]}

Svensson, Lennart ^{[1
]}

Svensson, Daniel

机构：

[1] Chalmers, Dept Signals & Syst, Gothenburg, Sweden

来源：

2014 17TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) | 2014年

关键词：

filtering; smoothing; Gaussian mixtures; forward-backward smoothing; data association; MULTIPLE TARGETS; ALGORITHM; TRACKING; REDUCTION;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Conventional forward-backward smoothing (FBS) for Gaussian mixture (GM) problems are based on pruning methods which yield a degenerate hypothesis tree and often lead to underestimated uncertainties. To overcome these shortcomings, we propose an algorithm that is based on merging components in the GM during filtering and smoothing. Compared to FBS based on the N-scan pruning, the proposed algorithm offers better performance in terms of track loss, root mean squared error (RMSE) and normalized estimation error squared (NEES) without increasing the computational complexity.

引用

页数：8

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