Intelligent Adaptive Gain Adjustment and Error Compensation for Improved Tracking Performance

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概要

• 論文の詳細を見る

• 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 non-traditional "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.

• 社団法人電子情報通信学会の論文
• 2000-11-25

著者

• Ko Hanseok

  The Author Is With The School Of Electrical Engineering Korea University

• Ahn Byungha

  The Authors Are With The Department Of Mechatronics K-jist

• CHO Kyungho

  The authors are with the Department of Mechatronics, K-JIST

• Cho Kyungho

  The Authors Are With The Department Of Mechatronics K-jist

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