The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
표적 추적에는 표준 칼만 필터(또는 α-β 필터)가 일반적으로 사용되지만, 표적이 심하게 움직일 때 필터 성능이 저하되는 경우가 많다는 것은 잘 알려져 있습니다. 조작을 수용하는 일반적인 방법은 필터 게인을 적응적으로 조정하는 것입니다. 우리의 목표는 비전통적인 "지능형" 알고리즘의 조합을 사용하여 실질적인 조작 중에 추적 오류를 줄이는 것입니다. 특히, 퍼지 규칙을 이용한 효과적인 이득 제어와 신경망을 통한 위치 오차 보상을 제안한다. 제안된 알고리즘을 적용하여 대표적인 기동의 다양한 목표 경로에 대해 몬테카를로 시뮬레이션을 수행하였다. 시뮬레이션 결과는 안정성, 정확성 및 계산 부하 측면에서 기존 방법에 비해 크게 개선되었음을 나타냅니다.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
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
부
@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},}
부
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 -