Adaptive Huber-Based Filter for Hypersonic Cruise Vehicle Navigation
The navigation for hypersonic cruise vehicle (HCV) is a challenging task because of the complex vehicle dynamic and sensor measurement noise it suffered. This paper proposes a kind of adaptive robust Kalman filter using Mahalanobis distance for HCV navigation. The innovation-based adaptive estimatio...
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Online Access: | https://doi.org/10.1260/1748-3018.8.3.319 |
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doaj-0463b03f9969490197f01a2c322a08662020-11-25T03:45:06ZengSAGE PublishingJournal of Algorithms & Computational Technology1748-30181748-30262014-09-01810.1260/1748-3018.8.3.319Adaptive Huber-Based Filter for Hypersonic Cruise Vehicle NavigationRong WangZhi XiongJianye LiuLina ZhongThe navigation for hypersonic cruise vehicle (HCV) is a challenging task because of the complex vehicle dynamic and sensor measurement noise it suffered. This paper proposes a kind of adaptive robust Kalman filter using Mahalanobis distance for HCV navigation. The innovation-based adaptive estimation is discussed first. Based on Mahalanobis distance theory, a kind of robust covariance matrix estimation method is used to modify the innovation-based adaptive Kalman filter. Considering that the vehicle maneuver characteristics and noise statistics parameters varies during different periods, dual-frequency tuning for unknown noise statistics is designed based on this. The algorithm proposed by this paper is applied to hypersonic cruise vehicle navigation. Simulation has been made to verify the performance of the new algorithm according to HCV flight profile and characteristics; both Gaussian and non-Gaussian simulation are included.https://doi.org/10.1260/1748-3018.8.3.319 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Rong Wang Zhi Xiong Jianye Liu Lina Zhong |
spellingShingle |
Rong Wang Zhi Xiong Jianye Liu Lina Zhong Adaptive Huber-Based Filter for Hypersonic Cruise Vehicle Navigation Journal of Algorithms & Computational Technology |
author_facet |
Rong Wang Zhi Xiong Jianye Liu Lina Zhong |
author_sort |
Rong Wang |
title |
Adaptive Huber-Based Filter for Hypersonic Cruise Vehicle Navigation |
title_short |
Adaptive Huber-Based Filter for Hypersonic Cruise Vehicle Navigation |
title_full |
Adaptive Huber-Based Filter for Hypersonic Cruise Vehicle Navigation |
title_fullStr |
Adaptive Huber-Based Filter for Hypersonic Cruise Vehicle Navigation |
title_full_unstemmed |
Adaptive Huber-Based Filter for Hypersonic Cruise Vehicle Navigation |
title_sort |
adaptive huber-based filter for hypersonic cruise vehicle navigation |
publisher |
SAGE Publishing |
series |
Journal of Algorithms & Computational Technology |
issn |
1748-3018 1748-3026 |
publishDate |
2014-09-01 |
description |
The navigation for hypersonic cruise vehicle (HCV) is a challenging task because of the complex vehicle dynamic and sensor measurement noise it suffered. This paper proposes a kind of adaptive robust Kalman filter using Mahalanobis distance for HCV navigation. The innovation-based adaptive estimation is discussed first. Based on Mahalanobis distance theory, a kind of robust covariance matrix estimation method is used to modify the innovation-based adaptive Kalman filter. Considering that the vehicle maneuver characteristics and noise statistics parameters varies during different periods, dual-frequency tuning for unknown noise statistics is designed based on this. The algorithm proposed by this paper is applied to hypersonic cruise vehicle navigation. Simulation has been made to verify the performance of the new algorithm according to HCV flight profile and characteristics; both Gaussian and non-Gaussian simulation are included. |
url |
https://doi.org/10.1260/1748-3018.8.3.319 |
work_keys_str_mv |
AT rongwang adaptivehuberbasedfilterforhypersoniccruisevehiclenavigation AT zhixiong adaptivehuberbasedfilterforhypersoniccruisevehiclenavigation AT jianyeliu adaptivehuberbasedfilterforhypersoniccruisevehiclenavigation AT linazhong adaptivehuberbasedfilterforhypersoniccruisevehiclenavigation |
_version_ |
1724511331273408512 |