Intelligent Adaptive Gain Adjustment and Error Compensation for Improved Tracking Performance
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E83-D No.11 pp.1952-1959
- Publication Date
- 2000/11/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Cognitive Science
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Kyungho CHO, Byungha AHN, Hanseok KO, "Intelligent Adaptive Gain Adjustment and Error Compensation for Improved Tracking Performance" in IEICE TRANSACTIONS on Information,
vol. E83-D, no. 11, pp. 1952-1959, November 2000, doi: .
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 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_11_1952/_p
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@ARTICLE{e83-d_11_1952,
author={Kyungho CHO, Byungha AHN, Hanseok KO, },
journal={IEICE TRANSACTIONS on Information},
title={Intelligent Adaptive Gain Adjustment and Error Compensation for Improved Tracking Performance},
year={2000},
volume={E83-D},
number={11},
pages={1952-1959},
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 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.},
keywords={},
doi={},
ISSN={},
month={November},}
Copy
TY - JOUR
TI - Intelligent Adaptive Gain Adjustment and Error Compensation for Improved Tracking Performance
T2 - IEICE TRANSACTIONS on Information
SP - 1952
EP - 1959
AU - Kyungho CHO
AU - Byungha AHN
AU - Hanseok KO
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E83-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2000
AB - 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.
ER -
English
