基于低成本多传感器的自适应组合滤波

韩辅君; 徐静; 宋世忠

doi:10.3788/OPE.20111912.3007

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基于低成本多传感器的自适应组合滤波

信息科学 | 更新时间：2020-08-12

- 基于低成本多传感器的自适应组合滤波
- Adaptive attitude estimation filtering with low-cost multi-sensors for MAHRS
- 光学精密工程 2011年19卷第12期页码：3007-3015
- 作者机构：
  
  1. 鲁东大学信息与电气学院,山东烟台,264025
  2. 济南职业学院机械制造系,山东济南 250001
  3. 济南军区,山东济南 250000
- 作者简介：
- 基金信息：
  
  鲁东大学引进人才项目(No.LY2011014)()
- DOI：10.3788/OPE.20111912.3007
  中图分类号： V249.32
- 收稿日期：2010-12-01，
  
  修回日期：2011-02-15，
  
  网络出版日期：2011-12-25，
  
  纸质出版日期：2011-12-25
- 稿件说明：
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韩辅君, 徐静, 宋世忠. 基于低成本多传感器的自适应组合滤波[J]. 光学精密工程, 2011,19(12): 3007-3015

HAN Fu-jun, XU Jing, SONG Shi-zhong. Adaptive attitude estimation filtering with low-cost multi-sensors for MAHRS[J]. Editorial Office of Optics and Precision Engineering, 2011,19(12): 3007-3015
韩辅君, 徐静, 宋世忠. 基于低成本多传感器的自适应组合滤波[J]. 光学精密工程, 2011,19(12): 3007-3015 DOI： 10.3788/OPE.20111912.3007.

HAN Fu-jun, XU Jing, SONG Shi-zhong. Adaptive attitude estimation filtering with low-cost multi-sensors for MAHRS[J]. Editorial Office of Optics and Precision Engineering, 2011,19(12): 3007-3015 DOI： 10.3788/OPE.20111912.3007.

摘要

研究了基于硅微机电系统(MEMS)陀螺、加速度计及磁阻式磁强计组合的微小型飞行器用姿态航向参考系统。针对传统航姿算法无法保证微小型飞行器在长时间、高机动情况下以较高精度保持姿态航向的问题

提出了一种基于低成本多传感器的自适应组合滤波算法。该算法首先通过对运动加速度和磁干扰进行建模并将其引入状态方程来保证载体在长时间高机动情况下依然保持较高的姿态航向精度。其次

采用联邦滤波模式降低运动加速度与磁干扰之间的相互影响

提高算法的精度和可靠性;通过对子滤波器专有状态量的估计方差阵P和量测方差R进行自适应设计

保证不同机动状态切换时算法的稳定性一致。用不同的组合算法进行了半物理仿真试验对比

结果表明

与传统算法相比

该算法在平飞和长时间盘旋飞行条件下均具有较高的精度和鲁棒性。

Abstract

An integrated Micro Attitude Heading Reference System(MAHRS) based on Micro-electro-mechanical System(MEMS) gyroscopes

accelerometers

and magnetic-sensors was researched for Micro Aerial Vehicles (MAV). As traditional algorithms could not keep the attitude accuracy of the MAV during a long-time maneuvering

an adaptive attitude estimation filtering algorithm with low-cost multi-sensors was presented for the MAHRS. Firstly

this algorithm was used to establish the models of acceleration and magnetic-disturbance and took them into a state equation. In this way

it could maintain the attitude accuracy of loads during the long-time maneuvering. Then

this algorithm adopted the federated filter mode to reduce the interaction between acceleration and magnetic disturbance to improve its precision and reliability. Moreover

the algorithm made the estimation variance (P) and measurement variance (R) remain steady adaptively during different maneuvering states. By comparing with other different algorithms for the MAHRS

It shows that the presented algorithm is more accurate and reliable than other algorithms during various maneuvering states.

关键词

Keywords

references

李荣冰, 刘建业, 赖际舟,等. 微小型飞行器惯性组合姿态确定与航路导航研究[J]. 航空学报, 2008, 29(S1):178-183. LI R B,LIU J Y,LAI J ZH,et al. Research on inertial integrated attitude determination and waypoint navigation for micro air vehicles[J]. Acta Aeronautica Et Astronautica Sinica, 2008, 29(S1):178-183. (in Chinese)[2] 朱自强, 王晓璐, 吴宗成,等. 小型和微型无人机的气动特点和设计[J]. 航空学报, 2006, 27(3):353-364. ZHU Z Q,WANG X L,WU Z CH,et al.. Aerodynamic characteristics of small/micro unmanned aerial vehicles and their shape design[J]. Acta Aeronautica Et Astronautica Sinica, 2006, 27(3):353-364. (in Chinese)[3] EGZIABHER G, HWYWARD D, HAYWARD R C, et al. A low cost GPS/inertial attitude heading reference system (AHRS) for general aviation applications.In proceedings of the IEEE Position Location and Navigation Symposium (PLANS), 1998:518-525.[4] HONG L, RUAN Y, LI W, et al. Energy-based video tracking using joint target density processing with an application to unmanned aerial vehicle surveillance[J]. Computer Vision, 2008, 1(2):1-12.[5] CONTE G, DOHERTY P. An Integrated UAV navigation system based on aerial image matching.Aerospace Conference, 2008:1-10.[6] WENDEL J, MEISTER O, SCHLAILE C, et al. An integrated GPS/MEMS-IMU navigation system for an autonomous helicopter[J]. Aerospace Science and Technology, 2006, 10:527-533.[7] SABATINI A M. Quaternion-based extended kalman filter for determining orientation by inertial and magnetic sensing[J]. IEEE Transactions on Biomedical Engineering, 2006, 53(7):1346-1356.[8] ZHU R, SUN D, ZHOU ZH Y, et al. A linear fusion algorithm for attitude determination using low cost MEMS based sensors[J]. Measurement, 2007, 40:322-328.[9] WANG M, YANG Y CH, RONALD R H, et al. Adaptive filter for a miniature MEMS based attitude and heading reference system.Record IEEE PLANS, Position, Location and Navigation Symposium, Piscataway, IEEE, Nanjing, 2004:193-200.[10] SUNG K H. Fuzzy logic based closed-loop strapdown attitude system for unmanned aerial vehicle(UAV)[J]. Sensor and Actuators, 2003, 107:109-118.[11] 杜亚玲, 刘建业, 刘瑞华. 捷联惯性航姿系统中的模糊内阻尼算法研究[J]. 南京航空航天大学学报, 2005,37(3): 274-278. DU Y L,LIU J Y,LIU R H. Fuzzy damped algorithm in strapdown attitude heading reference system[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2005, 37(3): 274-278. (in Chinese)[12] EUSTON M, COOTE P, MAHONY R, et al. A complementary filter for attitude estimation of a fixed-wing UAV.2008IEEE/RSJ International Conference on Intelligent Robots and System, France, 2008:340-345.[13] LIU CH, ZHOU ZH Y, FU X. Attitude determination for MAVs using a kalman filter[J]. Tsinghua Science and Technology, 2008, 13(5):593-597.[14] ROETENBERG D, LUINGW H J, BATEN C T, et al. Compensation of magnetic disturbance improves inertial and magnetic sensing of human body segment orientation[J]. IEEE Transaction on Neural Systems and Rehabilitation Engineering, 2005,13(3):395-405.[15] 郭鹏飞, 任章, 邱海韬. 机动加速度辅助的航姿系统扩展卡尔曼滤波[J]. 系统工程与电子技术, 2009, 31(3):625-628. GUO P F, REN ZH, QIU H T. Maneuvering acceleration assisted extended kalman filter for attitude and heading reference system[J]. Systems Engineering and Electronics, 2009, 31(3):625-628. (in Chinese)[16] 付梦印, 邓志红, 张继伟. Kalman滤波理论及其在导航系统中的应用[M]. 北京: 科学出版社, 2003:158-168. FU M Y, DENG ZH H, ZHANG J W. Kalman filtering theory and the applications in navigation system [M]. Beijing: Science Press, 2003:158-168.(in Chinese)

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