Applying Kalman filter-based fusion algorithm to estimation problems

Chung Lain Lu, Yi-Nung Chung, Chih Min Lin, Chin Chung Yu, Tsair Rong Chen

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

An algorithm denoted as Kalman filter-based fusion algorithm for estimation problems is developed in this paper. In this approach, a multiple-sensors data-fusion algorithm is applied. In order to solve the data association and target maneuvering situations, a computational logic, including 1-step conditional maximum likelihood and an adaptive estimator is applied to solve both data association and target maneuvering problems simultaneously. The advantage of this approach is that the multiple sensors can improve the tracking accuracy and the reliability of the radar surveillance. Computer simulation results indicate that this approach can successfully track multiple targets with satisfactory performance. ICIC International

Original languageEnglish
Pages (from-to)2109-2114
Number of pages6
JournalICIC Express Letters
Volume4
Issue number6 A
Publication statusPublished - 2010 Dec 1

Fingerprint

Kalman filters
Fusion reactions
Surveillance radar
Sensor data fusion
Maximum likelihood
Sensors
Computer simulation

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Control and Systems Engineering

Cite this

Lu, Chung Lain ; Chung, Yi-Nung ; Lin, Chih Min ; Yu, Chin Chung ; Chen, Tsair Rong. / Applying Kalman filter-based fusion algorithm to estimation problems. In: ICIC Express Letters. 2010 ; Vol. 4, No. 6 A. pp. 2109-2114.
@article{2c729ddf4c7b4427b86149dea16d9b76,
title = "Applying Kalman filter-based fusion algorithm to estimation problems",
abstract = "An algorithm denoted as Kalman filter-based fusion algorithm for estimation problems is developed in this paper. In this approach, a multiple-sensors data-fusion algorithm is applied. In order to solve the data association and target maneuvering situations, a computational logic, including 1-step conditional maximum likelihood and an adaptive estimator is applied to solve both data association and target maneuvering problems simultaneously. The advantage of this approach is that the multiple sensors can improve the tracking accuracy and the reliability of the radar surveillance. Computer simulation results indicate that this approach can successfully track multiple targets with satisfactory performance. ICIC International",
author = "Lu, {Chung Lain} and Yi-Nung Chung and Lin, {Chih Min} and Yu, {Chin Chung} and Chen, {Tsair Rong}",
year = "2010",
month = "12",
day = "1",
language = "English",
volume = "4",
pages = "2109--2114",
journal = "ICIC Express Letters",
issn = "1881-803X",
publisher = "ICIC Express Letters Office",
number = "6 A",

}

Applying Kalman filter-based fusion algorithm to estimation problems. / Lu, Chung Lain; Chung, Yi-Nung; Lin, Chih Min; Yu, Chin Chung; Chen, Tsair Rong.

In: ICIC Express Letters, Vol. 4, No. 6 A, 01.12.2010, p. 2109-2114.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Applying Kalman filter-based fusion algorithm to estimation problems

AU - Lu, Chung Lain

AU - Chung, Yi-Nung

AU - Lin, Chih Min

AU - Yu, Chin Chung

AU - Chen, Tsair Rong

PY - 2010/12/1

Y1 - 2010/12/1

N2 - An algorithm denoted as Kalman filter-based fusion algorithm for estimation problems is developed in this paper. In this approach, a multiple-sensors data-fusion algorithm is applied. In order to solve the data association and target maneuvering situations, a computational logic, including 1-step conditional maximum likelihood and an adaptive estimator is applied to solve both data association and target maneuvering problems simultaneously. The advantage of this approach is that the multiple sensors can improve the tracking accuracy and the reliability of the radar surveillance. Computer simulation results indicate that this approach can successfully track multiple targets with satisfactory performance. ICIC International

AB - An algorithm denoted as Kalman filter-based fusion algorithm for estimation problems is developed in this paper. In this approach, a multiple-sensors data-fusion algorithm is applied. In order to solve the data association and target maneuvering situations, a computational logic, including 1-step conditional maximum likelihood and an adaptive estimator is applied to solve both data association and target maneuvering problems simultaneously. The advantage of this approach is that the multiple sensors can improve the tracking accuracy and the reliability of the radar surveillance. Computer simulation results indicate that this approach can successfully track multiple targets with satisfactory performance. ICIC International

UR - http://www.scopus.com/inward/record.url?scp=78650258246&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78650258246&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:78650258246

VL - 4

SP - 2109

EP - 2114

JO - ICIC Express Letters

JF - ICIC Express Letters

SN - 1881-803X

IS - 6 A

ER -