Intelligent adaptive gain adjustment and error compensation for improved tracking performance

Kyungho Cho, Byungha Ahn, Hanseok Ko

Research output: Contribution to journalArticle

Abstract

While a standard Kalman filter (or α-β filter) is commonly used for target tracking, it is well known that the filter performance is often degraded when the target heavily maneuvers. The usual way to accommodate maneuver is to adaptively adjust the filter gain. Our aim is to reduce the tracking error during substantial maneuvering using a combination of nontraditional "intelligent" algorithms. In particular, we propose an effective gain control using fuzzy rule followed by position error compensation via neural network. A Monte-Carlo simulation is performed for various target paths of representative maneuvers employing the proposed algorithm. The results of the simulation indicate a significant improvement over conventional methods in terms of stability, accuracy, and computational load.

Original languageEnglish
Pages (from-to)1952-1959
Number of pages8
JournalIEICE Transactions on Information and Systems
VolumeE83-D
Issue number11
Publication statusPublished - 2000
Externally publishedYes

Fingerprint

Error compensation
Gain control
Fuzzy rules
Target tracking
Kalman filters
Neural networks
Monte Carlo simulation

Keywords

  • Fuzzy rule base
  • Gain adjustment
  • Neural network
  • Tracking

ASJC Scopus subject areas

  • Information Systems
  • Computer Graphics and Computer-Aided Design
  • Software

Cite this

Intelligent adaptive gain adjustment and error compensation for improved tracking performance. / Cho, Kyungho; Ahn, Byungha; Ko, Hanseok.

In: IEICE Transactions on Information and Systems, Vol. E83-D, No. 11, 2000, p. 1952-1959.

Research output: Contribution to journalArticle

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