NDLI: Gaussian particle filtering algorithm for out-of-sequence-measurement problem

Content Provider	IEEE Xplore Digital Library
Author	Wang Wei Wang Gongbao Huang Xin-Han Wang Min Peng Gang
Copyright Year	2011
Description	Author affiliation: School of Science, Naval University of Engineering, Wuhan 430033, China (Wang Wei; Wang Gongbao) \|\| Dept. of Control Science and Engineering, Huazhong University of Science Technology, Wuhan 430074, China (Huang Xin-Han; Wang Min; Peng Gang)
Abstract	Using multiple sensors to track targets, due to communication delays and variable signal pre-processing time, will lead to out-of-sequence measurement problems. In this paper, based on sequential Bayesian formula and Gaussian particle filter, a novel out of sequence measurement processing algorithm is developed. In essence, this algorithm uses importance sampling to update the posterior means and their covariances, and also approximates the posterior distributes by single Gaussians. The algorithm has low complexity and its performance is consistent with standard sequential processing algorithm and it is asymptotically optimal as numbers of particles tends to infinity. This paper is the extended version of [15].
Starting Page	3297
Ending Page	3300
File Size	302615
Page Count	4
File Format	PDF
ISBN	9781424480364
e-ISBN	9781424480395
DOI	10.1109/ICEICE.2011.5777617
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-04-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Out-of-sequence-measurement Atmospheric measurements Sequence Bayesian estimation Aerospace electronics Selective fusion Gaussian particle filtering Particle measurements Approximation algorithms Radar tracking Particle filters
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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