Optimal update with out-of-sequence measurements

Keshu Zhang; X. Rong Li; Yunmin Zhu

doi:10.1109/TSP.2005.847830

Optimal update with out-of-sequence measurements

Keshu Zhang
, X. Rong Li
, Yunmin Zhu

University of New Orleans
Motorola
Sichuan University

Research output: Contribution to journal › Article › peer-review

146 Scopus citations

Abstract

This paper is concerned with optimal filtering in a distributed multiple sensor system with the so-called out-of-sequence measurements (OOSM). Based on best linear unbiased estimation (BLUE) fusion, we present two algorithms for updating with OOSM that are optimal for the information available at the time of update. Different minimum storages of information concerning the occurrence time of OOSMs are given for both algorithms. It is shown by analysis and simulation results that the two proposed algorithms are flexible and simple.

Original language	English
Pages (from-to)	1992-2004
Number of pages	13
Journal	IEEE Transactions on Signal Processing
Volume	53
Issue number	6
DOIs	https://doi.org/10.1109/TSP.2005.847830
State	Published - Jun 2005
Externally published	Yes

Keywords

Kalman filter
LMMSE
Out-of-sequence measurement
Target tracking

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10.1109/TSP.2005.847830

Cite this

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title = "Optimal update with out-of-sequence measurements",

abstract = "This paper is concerned with optimal filtering in a distributed multiple sensor system with the so-called out-of-sequence measurements (OOSM). Based on best linear unbiased estimation (BLUE) fusion, we present two algorithms for updating with OOSM that are optimal for the information available at the time of update. Different minimum storages of information concerning the occurrence time of OOSMs are given for both algorithms. It is shown by analysis and simulation results that the two proposed algorithms are flexible and simple.",

keywords = "Kalman filter, LMMSE, Out-of-sequence measurement, Target tracking",

author = "Keshu Zhang and Li, \{X. Rong\} and Yunmin Zhu",

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AB - This paper is concerned with optimal filtering in a distributed multiple sensor system with the so-called out-of-sequence measurements (OOSM). Based on best linear unbiased estimation (BLUE) fusion, we present two algorithms for updating with OOSM that are optimal for the information available at the time of update. Different minimum storages of information concerning the occurrence time of OOSMs are given for both algorithms. It is shown by analysis and simulation results that the two proposed algorithms are flexible and simple.

KW - Kalman filter

KW - LMMSE

KW - Out-of-sequence measurement

KW - Target tracking

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M3 - 文章

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JO - IEEE Transactions on Signal Processing

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ER -

Optimal update with out-of-sequence measurements

Abstract

Keywords

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