摘要
“协同编队飞行”是一种全新的无人机应用模式,具有广阔的前景和巨大的技术优势,代表了无人机技术的未来发展方向,因而备受关注。本文以无人机协同编队飞行为背景,对编队无人机之间的相对导航问题展开研究,通过对多种相对导航方案的对比,最终确定了惯导系统和VisNav视觉导航系统所组成的INS/VisNav相对导航系统方案,并设计了多种相对导航滤波器,主要的内容包括:
针对“长机——僚机”编队模式,推导了无人机之间的相对运动模型,给出了陀螺、加速度计和VisNav视觉导航系统的测量模型,对VisNav视觉导航系统在单矢量、双矢量、三矢量以及多矢量观测情况下可观测性进行了分析,为确定VisNav视觉导航系统的工作条件提供了理论依据。采用最小二乘法处理VisNav视觉导航系统的输出数据,估计出编队无人机之间的相对姿态和相对位置。数学仿真分析显示,最小二乘法的估计精度受无人机之间的相对位置关系所影响。
设计了基于扩展卡尔曼滤波算法的INS/VisNav相对导航滤波器。用长机和僚机的惯性导航系统测量数据代替相对运动方程中的角速度和加速度项,获得长机和僚机之间的相对惯导方程,以相对惯导方程为基础,推导了扩展卡尔曼滤波中的雅可比矩阵和测量敏感矩阵。考虑到长机与僚机处于编队飞行模式中,忽略重力项影响,从而简化了扩展卡尔曼滤波算法,提高了运算速度。
为了改善收敛性能,并提高估计精度,设计了基于Sigma-Point卡尔曼滤波算法的INS/VisNav相对导航滤波器。介绍了Unscented卡尔曼滤波算法和中心差分卡尔曼滤波算法,通过对比分析上述两种算法发现,二者的共同点是利用Sigma-Point近似非线性函数。在设计Sigma-Point卡尔曼滤波器时,考虑到姿态四元数的归一化限制,采用罗德里格斯参数代表姿态误差状态,保证了误差协方差矩阵的非奇异性,仿真结果显示,算法具有明显的快速收敛性能。
考虑到当系统噪声不符合高斯分布时,卡尔曼滤波难以取得高精度估计,甚至发散,设计了基于Huber-Based滤波的INS/VisNav相对导航滤波器,Huber-Based滤波器是一种鲁棒滤波器,建立在l1、l2混合范数最小的基础上,当系统噪声为受污染的高斯分布时,该滤波方法具有一定的鲁棒性。将Huber-Based滤波与Sigma-Point滤波相结合,提出了一种鲁棒Sigma-Point滤波方法,并设计了基于鲁棒Sigma-Point滤波的相对导航滤波器,最后进行了数学仿真研究。
精度和可靠性是衡量相对导航系统性能的两个重要准则,本文提出了一种INS/VisNav/GPS多传感器信息融合相对导航系统,在分布式融合结构下,采用信息滤波算法融合INS、VisNav和GPS载波相位差分系统输出,与INS/VisNav相对导航系统相比,该系统实现了精度和可靠性的双重提高。
Cooperative formation flight is a kind of new application method of Unmanned Aerial Vehicle (UAV) which has a wide prospect and huge technical advantage and represents the future developing direction. Therefore, many researchers pay attention to cooperative formation flight. This paper whose background is cooperative formation flight does research on relative navigation between UAVs in formation. By contrast with multiple relative navigation schemes, INS/VisNav is chosen as the project whose core equipment is VisNav. In the same time, various relative navigation filters are designed. The major contents are as follows:
On the basis of introducing the attitude description and attitude kinematics of UAV, according to the“leader-follower”formation mode, the relative kinematics model is derived, the measurement models of gyro, accelerometer and VisNav are provided, and the observability of VisNav is analyzed on the condition of single vector, double vectors, three vectors and multiple vectors measurements. Therefore, the working premise of VisNav could be determined. The least square algorithm is applied to process the data of VisNav to estimate the relative attitude and position. The results of simulation show that the accuracy of least square algorithm would be influenced by the relative position of UAVs in formation.
The INS/VisNav relative navigation filter is designed which is based on extended Kalman filtering (EKF). The INS measurement dates are used to substitute the angle velocity and acceleration in relative motion equations. As a result, the relative INS equations could be acquired. In the same time, the Jacobian matrix and measurement matrix of EKF could be derived. In the premise of formation flight of leader and follower, the effect of gravity could be ignored. Therefore, the algorithm is simplified and the calculation velocity is promoted.
To improve the convergence rate and estimated accuracy, the relative navigation filter is designed which is based on Sigma-Point Kalman filtering. Firstly, the Unscented Kalman filtering and Central Differential Kalman filtering are introduced. By contrast the two algorithms, the common points could be found which are that the Sigma-Point is used to approximate the nonlinear equations. As a result, the Sigma-Point Kalman filtering is summarized and concluded. For the quaternion normalization, an unconstrained three-component vector is used to presented an attitude error quaternion which could confirm the non-singular of error covariance matrix. The simulation results show that the algorithm has evident quick convergence rate.
Considering that when the system noise follows non-Gaussian probability distribution, the Kalman filtering couldn’t estimate accurately, filter divergence would be possible to happen on extreme condition. The relative navigation filter is designed which is based on Huber-Based filtering that is a combined minimum l1 and l2 norm estimator which could exhibit robustness for the perturbed Gaussian probability distribution. The Huber-Based filtering and Sigma-Point Kalman filtering are combined. As a result, a new algorithm which is called robust Sigma-Point Kalman filtering is presented. The relative navigation filter is designed which is based on robust Sigma-Point Kalman filtering. Finally, the simulation was done.
The accuracy and reliability are two important standards which are used to evaluate the relative navigation system. This paper presents the INS/VisNav/GPS relative navigation system. Using the hierarchically decentralized structure, the information filtering is applied to fuse the dates from INS, VisNav and GPS system. Contrast with INS/VisNav, this system could promote the accuracy and reliability.
引文
1 M. Pacher, J. J. D’Azzo and A. W. Proud. Tight Formation Flight Control. Journal of Guidance, Control, and Dynamics. 2001, 24(2):246-254
2 E. Lavretsky. F/A-18 Autonomous Formation Flight Control System Design. Proceedings of AIAA Guidance, Navigation, and Control Conference and Exhibit. Monterey, California, AIAA-2002-4757
3 Y. F. Zou, P. R. Pagilla and R. T. Ratliff. Distributed Formation Flight Control Using Constraint Forces. Journal of Guidance, Control, and Dynamics. 2009, 32(1):112-120
4 D. J. Pack and G.W. P. York. Developing a Control Architecture for Multiple Unmanned Aerial Vehicles to Search and Localize RF Time-Varying Mobile Targets: PartⅠ. Proceedings of the 2005 IEEE International Conference on Robotics and Automation. Barcelona, Spain, 2005:3954-3959
5 G. L. Plett, P. D. Lima and D. J. Pack. Target Localization using Multiple UAVs with Sensor Fusion via Sigma-Point Kalman Filtering. Proceedings of AIAA Infotech Aerospace 2007 Conference and Exhibit. Rohnert, California, AIAA-2007-2845
6 G. J. Toussaint, P. D. Lima and D. J. Pack. Localizing RF Targets with Cooperative Unmanned Aerial Vehicles. Proceedings of the 2007 American Control Conference. New York City, UAS, 2007:5928-5933
7 J. Ousingsawat and M. E. Campbell. Multiple Vehicle Team Tasking for Cooperative Estimation. Available: citeseer.ist.psu.edu/679960.html
8 J. Kim and J. P. Hespanha. Cooperative Radar Jamming for Groups of Unmanned Air Vehicles. Proceedings of 43rd IEEE Conference on Decision and Control. Paradise Island, Bahamas, 2004:632-637
9 Y. Eun and H. Bang. Cooperative Control of Multiple UCAVs for Suppression of Enemy Air Defense. Proceedings of AIAA 3rd“Unmanned Unlimited”Technical Conference, Workshop and Exhibit. Chicago, IIIinois, AIAA-2004-6529
10 C. A. Lua, K. Altenburg and K. E. Nygard. Synchronized Multi-Point Attack by Autonomous Reactive Vehicles with Simple Local Communication.Proceedings of the 2003 IEEE Swarm Intelligence Symposium. Indianapolis, Indiana, 2003:95-102
11刘立恒.鲁棒滤波及其在导航系统中的应用.哈尔滨工业大学博士论文. 2003:10-12
12秦永元,张洪钺,汪叔华.卡尔曼滤波与组合导航原理.西北工业大学出版社. 2004:238-239
13章燕申.高精度导航系统.中国宇航出版社. 2005:22-25
14 D. J. Pasik, M. I. Gneses and G. R. Taylor. A Ring Laser Gyro Strapdown Inertial Navigation System Performance Analysis and Test Results. Proceedings of AIAA Guidance and Control Conference. Boston, AIAA-75-1095
15 D. W. Harriman and J. C. Harrison. Gravity-Induced Errors in Airborne Inertial Navigation. Journal of Guidance, Control, and Dynamics. 1986, 9(4):419-426
16 I. Y. Bar-Itzhack, D. Serfaty and Y. Vitek. Doppler-Aided Low-Accuracy Strapdown Inertial Navigation System. Journal of Guidance, Control, and Dynamics. 1982, 5(3):236-242
17 S. P. Kau, A. A. Morgan and R. F. Malzahn. Laser Gyro for Instrumentation of Advanced Missile Guidance System Testing. Proceedings of Guidance and Control Conference. New York, 1983:700-707
18 J. F. Klafin. Tactical Flight Management System Design. Proceedings of AIAA Aircraft Design, Systems and Technology Meeting. Fort Worth, Texas, AIAA-83-2559
19 C. S. G. Jr. Performance of a Ring Laser Strapdown Attitude and Heading Reference for Aircraft. Journal of Guidance, Control, and Dynamics. 1978, 2(4):320-327
20 S. G. Hummel and E. L. Tesch. Laser Gyros in Precision Spacecraft Attitude Determination Systems. Proceedings of Guidance and Control Conference. Boulder, Colo, AIAA-1979-1716
21 J. C. Ha, E. Kanegsberg and R. A. Patterson. Fiber Optic Gyro Development for Precision Guidance and Navigation Applications. Proceedings of AIAA Guidance, Navigation and Control Conference. New Orleans, AIAA-1997-3548
22 S. Divakaruni, G. Keith, C. Narayanan and J. L. Keener. Strategic Interferometric Fiber-Optic Gyroscope for Ballistic Missile Inertial Guidance. Proceedings of AIAA Guidance, Navigation and Control Conference and Exhibit. Honolulu, Hawaii, AIAA-2008-7301
23 R. Y. Liu, T. E. Wailly, M. Olson and G. Adams. Low Cost Tactical Grade FOG IMU. Proceedings of AIAA Guidance, Navigation and Control Conference. Baltimore, MD, AIAA-1995-3232
24 G. A. Pavlath. Development of Inertial-Grade Fiber Gyros. Proceedings of AIAA Guidance, Navigation and Control Conference. Baltimore, MD, AIAA-1995-3231
25 K. Schadow. MEMS Milatary Applications-RTO Task Group Summary. Proceedings of CANEUS 2004-Conference on Micro-Nano-Technologies. Monterey, California, AIAA-2004-6749
26 A. Huang, C. Folk, C. Silva, et al. Application of MEMS Devives to Delta Wing Aircraft: From Concept Dvelopment to Transonic Flight Test. Proceedings of 39th AIAA Aerospace Sciences Meeting and Exhibit. Reno, Nevada, AIAA-2001-124
27 L. K. D. Garon, G. L. Abate and W. Hathaway. Free-Flight Testing of Generic Missile with MEMS Protuberances. Proceedings of 41st Aerospace Sciences Meeting and Exhibit. Reno, Nevada, AIAA-2003-1242
28李跃,邱致和.导航与定位.国防工业出版社. 2008:18-21
29 B. W. Parkinson. The Global Positioning System. Bulletin Geodesique. 1979:89-108
30 B. W. Parkinson and P. Enge. Global Positioning System:Theory and Application. AIAA. 1996:5-23
31 R. B. Langley. Glonass: Review and Update. GPS World. July, 1997:46-51.
32 W. J. O’Neil. Project Galileo. Proceedings of AIAA Space Programs and Technologies Conference. Huntsville, AL, AIAA-1990-3854
33 C. L. Gilera. GNSS Third Party Liability: The European Experience of Galileo. Proceedings of 57th International Astronautical Congress. Valencia, Spain, AIAA-2006-852
34 C. Cantelmo, R. Zanello, M. Blanchi, et al. Galileo Timing Applications and ACTS Prototyping. Proceedings of 2009 Joint with the 22nd EuropeanFrequency and Time Forum. 2009:405-410
35 J. George, C. Kunstmann, S. Southard, et al. Galileo System Design for Orbital Operations. Proceedings of 13th Digital Avionics Systems Conference. 1994:40-45
36 J. C. Marr. Performing the Galileo Mission Using the S-Band Low-Gain Antenna. Proceedings of Aerospace Applications Conference. 1994:145-183
37谭述森.北斗卫星导航系统的发展与思考.宇航学报. 2008, 29(2):391-396.
38李俊锋.“北斗”卫星导航定位系统与全球定位系统之比较分析.北京测绘. 2007, (1):51-53
39 K. Gunnam, D. C. Hughes and J. L. Junkins, N. Kehtarnavaz. A DSP Embedded Optical Navigation System. Proceedings of 2002 6th International Conference on Signal Processing. 2002:1735-1739
40 J. Kimmett, J. Valasek and J. L. Junkins. Autonomous Aerial Refueling Utilizing a Vision Based Navigation System. Proceedings of AIAA Guidance, Navigation and Control Conference and Exhibit. Monterey, California, AIAA-2002-4469
41 K. K. Gunnam, D. C. Hughes, J. L. Junkins and N. Kehtarnavaz. A Vision-Based DSP Embedded Navigation Sensor. IEEE Sensors Journal. 2002, 2(5):428-442
42 J. Doebbler, J. J. Davis, J. Valasek and J. L. Junkins. Characterization and Implementation of a Vision-Based 6-DOF Localization System. Proceedings of AIAA Guidance, Navigation and Control Conference and Exhibit. Honolulu, Hawaii, AIAA-2008-7321
43 V. Stepanyan, E. Lavretsky and N. Hovakimyan. Aerial Refueling Autopilot Design Methodology: Application to F-16 Aircraft Model. Proceedings of AIAA Guidance, Navigation and Control Conference and Exhibit. Providence, Rhode Island, AIAA-2004-5321
44 M. Herrnberger, G. Sachs, F. Holzapfel, et al. Simulation Analysis of Autonomous Aerial Refueling Procedures. Proceedings of AIAA Guidance, Navigation and Control Conference and Exhibit. San Francisco, California, AIAA-2005-5866
45 M. D. Tandale, R. Bowers and J. Valasek. Robust Trajectory Tracking Controller for Vision Based Probe and Drogue Autonomous Aerial Refueling.Proceedings of AIAA Guidance, Navigation and Control Conference and Exhibit. San Francisco, California, AIAA-2005-5868
46 J. Doebbler, J. Valasek, M. J. Monda and H. Schaub. Boom and Receptacle Autonomous Air Refueling Using a Visual Pressure Snake Optical Sensor. Proceedings of AIAA Atmospheric Flight Mechanics Conference and Exhibit. Keystone, Colorado, AIAA-2006-6504
47 M. Marwaha, J. Valasek and A. Narang. Fault Tolerant SAMI for Vision-Based Probe and Deogue Autonomous Aerial Refueling. Proceedings of AIAA Infotech Aerospace Conference and AIAA unmanned Unlimited Conference. Seattle, Washington, AIAA-2009-1887
48 M. L. Fravolini, G. Campa and M. R. Napolitano. Evaluation of Machine Vision Algorithms for Autonomous Aerial Refueling for Unmanned Aerial Vehicles. Journal of Aerospace Computing, Information and Communication. 2007, 4, 968-985
49 A. Dogan, S. Venkataramanan, and w. Blake. Modeling of Aerodynamic Coupling Between Aircraft in Close Proximity. Journal of Aircraft. 2005, 42(4):941-955
50 E. N. Johnson, A. J. Calise, Y. Watanabe, et al. Real-Time Vision-Based Relative Aircraft Navigation. Journal of Aerospace Computing, Information and Communication. 2007, 4,707-738
51 M. J. Tahk, C. S. Park and C. K. Ryoo. Line-of-Sight Gudance Laws for Formation Flight. Journal of Guidance, Control, and Dynamics. 2005, 28(4):708-716
52 Y. Watanabe, A. J. Calise and E. N. Johnson. Minimum-Effort Guidance for Vision-Based Collision Avoidance. Proceedings of AIAA Atmospheric Flight Mechinics Conference and Exhibit. Keystone, Colorado, AIAA-2006-6641
53 A. M. Fosbury and J. L. Crassdis. Relative Navigation of Air Vehicle. Journal of Guidance, Control, and Dynamics. 2008, 31(4):824-834
54 R. Linares, J. L. Crassdis and Y. Cheng. Constrained Relative Attitude Determination for Two Vehicle Formations. Proceedings of AIAA Guidance, Navigation and Control Conference. Chicago, IIIinois, AIAA-2009-5882
55 R. Sattigeri, A. J. Calise, B. S. Kim, et al. 6-DOF Nonlinear Simulation of Vision-based Formation Flight. Proceedings of AIAA Guidance, Navigationand Control Conference and Exhibit. San Francisco, California, AIAA-2005-6002
56 L. Pollini, R. Mati and M. Innocenti. Experimental Evaluation of Vision Algorithms for Formation Flight and Aerial Refueling. Proceedings of AIAA Modeling and Simulation Technologies Conference and Exhibit. Providence, Rhode Island, AIAA-2004-4918
57 L. Pollini, R. Mati, M. Innocenti, et al. A Synthetic Environment for Simulation of Vision-Based Formation Flight. Proceedings of AIAA Modeling and Simulation Technologies Conference and Exhibit. Austin, Texas, AIAA-2003-5376
58 K. Subbarao and A. Dogan. Intelligent Integrated Sensor, Control and Maneuver Mapping Architecture for Autonomous Vehicle Rendezvous and Docking. Proceedings of AIAA 3rd“Unmanned Unlimited”Technical Conference, Workshop and Exhibit. Chicago, IIIinois, AIAA-2004-6573
59 D. C. Woffinden and D. K. Geller. Relative Angles-Only Navigation and Pose Estimation for Autonomous Oribital Rendezvous. Journal of Guidance, Control, and Dynamics. 2007, 30(5):1455-1469
60 J. J. Junkins, Hughes, Declan, et al. Vision-Based Navigation for Rendezvous, Docking and Proximity Operations. Proceedings of 22nd Annual AAS Guidance and Control Conference. Breckenridge, Colorado, ASS-99-021
61 P. Calhoun and R. Dabney. A Solution to the Problem of Determining the Relative 6-DOF State for Spacecraft Automated Rendezvous and Docking. Proceedings of SPIE Space Guidance, Control, Tracking. Orlando, 1995:175-184
62 S. D. Lindell and W. S. Cook. Closed-Loop Autonomous Rendezvous and Capture Demonstration Based on Optical Pattern Recognition. Proceedings of 22nd Annual AAS Guidance Control Conference. 1999, AAS-99-027
63 M. Mokuno, I. Kawano and T. Kasai. Experimental Results of Autonomous Rendezvous Docking on Japanese ETS-VII Satellite. Proceedings of 22nd Annual AAS Guidance Control Conference. 1999, AAS-99-022
64 S. Kim, J. L. Crassdis, Y. Cheng, J. Junkins. Kalman Filtering for Relative Spacecraft Attitude and Position Estimation. Journal of Guidance, Control, and Dynamics. 2007, 30(1):133-143
65 R. Alonso. J. L. Crassidis, J. L. Junkins. Vision-Based Navigation for Formation Flying Spacecraft. Proceedings of AIAA Guidance, Navigation, and Control Conference. Denver, CO, AIAA-2000-4439
66 J. Maki, T. Litwin and M. Schwochert. Operation and Performance of the Mars Exploration Rover Imaging System on the Martian Surface. Proceedings of IEEE Int’1 Conf. Systems, Man and Cybernetics. Hawaii, USA, 2005:930-936
67 Y. Cheng, M. Maimone and L. Matthies. Visual Odometry on the Mars Exploration Rover. IEEE Robotics and Automation Magazine. 2006, 13(2):54-62
68 P. Leger, R. Deen and R. Bonitz. Remote Image Analysis for Mars Exploration Rover Mobility and Manipulation Operations. Proceedings of IEEE Int’1 Conf. Systems, Man and Cybernetics. Hawaii, USA, 2005:917-922
69 M. Vergauwen, M. Pollefeys and L. Goll. A Stereo Vision System for Support of Planetary Surface Exploration. Machine Vision and Application. 2003, 14(1):5-14
70 D. Murray, J. J. Little. Using Real-Time Stereo Vision for Mobile Robot Navigation. Autonomous Robots. 2000, 8(2):161-171
71侯建.月球车立体视觉与视觉导航方法研究.哈尔滨工业大学博士论文. 2007:5-20
72 H. Maeda, A. Itsukaichi, I. Kawano and M. Mokuno. GPS Relative Navigation System for Space Use. Proceedings of International Communications Satellite System Conference and Exhibit. Yokohama, Japan, AIAA-1998-1333
73 T. Ebinuma, R. H. Bishop and E. G. Integrated Hardware Investigation of Precision Spacecraft Rendezvous Using the Global Positioning System. Journal of Guidance, Control, and Dynamics. 2003, 26(3):425-433
74 F. D. Busse. Precise Formation-State Estimation in Low Earth Orbit Using Carrier Differential GPS. Ph.D Dissertion of Stanford. 2003:12-30
75 S. Mohiuddin and M. L. Psiaki. High-Altitude Satellite Relative Navigation Using Carrier-Phase Differential Global Positioning System Techniques. Journal of Guidance, Control, and Dynamics. 2007, 30(5):1427-1436
76 G. H. Purcell, D. Kuang, S.Lichten, S. C. Wu and L. E. Yong. AutonomousFormation Flyer (AFF) Sensor Technology Development. Proceedings of 21st Annual AAS Guidance and Control Conference. Breckenridge, Colorado, AAS-98-102
77 W. R. Williamson, M. F. Abdel-Hafez, I. Rhee, et al. An Instrumentation System Applied to Formation Flight. Proceedings of AIAA’s 1st Technique Conference and Workshop on Unmanned Aerospace Vehicles. Portsmouth, Virginia, AIAA-2002-3430
78 C. E. Hanson, J. Ryan, M. J. Allen, S. R. Jacobson. Flight Test Results for an Autonomous Formation Flight Control System. Proceedings of AIAA’s 1st Technique Conference and Workshop on Unmanned Aerospace Vehicles. Portsmouth, Virginia, AIAA-2002-3431
79 M. J. Vachon, R. J. Ray, K. R. Walsh and K. Ennix. F/A-18 Aircraft Performance Benefits Measured During the Autonomous Formation Flight Project. Proceedings of AIAA Atmospheric Flight Mechanics Conference and Exhibit. Monterey, California, AIAA-2002-4491
80 R. J. Ray, B. R. Cobleigh, M. J. Vahon and C. S. John. Flight Test Techniques Used to Evaluate Performance Benefits During Formation Flight. Proceedings of AIAA Atmospheric Flight Mechanics Conference and Exhibit. Monterey, California, AIAA-2002-4492
81 C. E. Hanson, J. Ryan, M. J. Allen and S. R. Jacobson. An Overview of Flight Test Results for a Formation Flight Autopilot. Proceedings of AIAA Atmospheric Flight Mechanics Conference and Exhibit. Monterey, California, AIAA-2002-4755
82 M. J. Allen, J. Ryan, C. E. Hanson and J. F. Parle. String Stability a Linear Formation Flight Control System. Proceedings of AIAA Atmospheric Flight Mechanics Conference and Exhibit. Monterey, California, AIAA-2002-4756
83 G. Bever, P. Urschel and C. Hanson. Comparison of Relative Navigation Solutions Applied between Two Aircraft. Proceedings of AIAA’s 1st Techinique Conference and Workshop on Unmanned Aerospace Vehicles. Portsmouth, Virginia, AIAA-2002-3440
84 A. M. Fosbury and J. L. Crassidis. Optimal Trajectory Determination for Increased Relative Navigation Observability of Air Vehicles. Proceedings of AIAA Guidance, Navigation and Control Conference and Exhibit. Honolulu,Hawaii, AIAA-2008-6648
85 A. M. Fosbury and J. L. Crassdis. Kalman Filtering for Relative Inertial Navigation of Uninhabited Air Vehicles. Proceedings of AIAA Guidance, Navigation and Control Conference and Exhibit. Keystone, Colorado, AIAA-2006-6544
86 A. M. Fosbury. Control and Kalman Filtering for Relative Dynamics of a Formation of Uninhabited Autonomous Vehicles. Ph.D Dissertion of Buffalo. 2006: 48-84
87 G. Campa, M. L. Fravolini, A. Ficola, et al. Autonomous Aerial Refueling for UAVs Using a Combined GPS-Machine Vision Guidance. Proceedings of AIAA Guidance, Navigation and Control Conference and Exhibit. Providence, Rhode Island, AIAA-2004-5350
88 W. R. Williamson, G. J. Glenn, J. L. Speyer, et al. Sensor Fusion Applied to Autonomous Aerail Refueling. Journal of Guidance, Control, and Dynamics. 2009, 32(1):262-275.
89 J. R. Yim, J. L. Crassidis, J. L. Junkins. Autonomous Orbit Navigation of Two Spacecraft System Using Relative Line of Sight Vector Measurements. American Astronautical Society. 2004, AAS-04-257
90 J. L. Junkins, D. C. Hughes, K. P. Wazni and V. Pariyapong. Vision-Based Navigation for Rendezvous and Docking and Proximity Operations. Proceedings of 22nd Annual AAS Guidance and Control Conference. Breckenridge, CO, AAS-99-021
91 R. T. Howard and T. C. Bryan. Video Guidance Sensor for Automated Capture. Proceedings of Space Programmes and Technologies Conference. Huntsville, AL, AIAA-1992-1389
92 R. Mukundan, R. R. Narayanan and N. Philip. A Vision Based Attitude and Position Estimation Algorithm for Rendezvous and Docking. Journal of Spacecraft Technology: Windous Magazine. 1994, 4(2):60-66
93 Y. Cheng, J. L. Crassidis and F. L. Markley. Attitude Esitmation for Large Field-of-View Sensors. Proceedings of AAS Malcolm D. Shuster Astronautic Symposium. Grand Island, NY, AAS-2005-462
94 A. M. Fosbury. Satellite Position and Attitude Estimation Using an Extended Kalman Filter. Proceedings of AIAA Guidance, Navigation and ControlConference and Exhibit. Hilton Head, South Carolina, AIAA-2007-6660
95 S. Kim, J. L. Crassidis, Y. Cheng, et al. Kalman Filtering for Relative Spacecraft Attitude and Position Estimation
96 K. Subbarao and J. McDonald. Multi-Sensor Fusion Based Relative Navigation for Synchronization and Capture of Free Floating Spacecraft. Proceedings of AIAA Guidance, Navigation and Control Conference and Exhibit. San Francisco, California, AIAA-2005-5858
97 R. E. Kalman and R. S. Bucy. New Results in Linear Filtering and Prediction Theory. Journal of Basic Eng (ASME). 1960, 82D:95-108
98胡士强,敬忠良.粒子滤波算法综述.控制与决策. 2005, 20(4):361-365
99 R. S. Bucy and K. D. Renne. Digital synthesis of nonlinear filter. Automatic. 1971, 7(3):287-289
100 Y. Sunahara. An Approximate Method of State Estimation for Nonlinear Dynamical Systems. Proceedings of Joint Automatic Control Conference. University of Colorado, 1969
101 J. Metzger, J. Wendel, G. Trommer, et al. Hybrid Terrain Referenced Navigations System Using Kalman Filters and Comparison Techniques. Proceedings of AIAA Guidance, Navigation and Control Conference and Exhibit. Providence, Rhode Island, AIAA-2004-5126
102 Satellite Attitude Determination Using Magnetometer Date Only. Proceedings of 47th AIAA Aerospace Sciences Meeting Including the New Forum and Aerospace Exposition. Orlando, Florida, AIAA-2009-220
103 D. Pool, Q. Chu, M. V. Passen and M. Mulder. Optimal Reconstruction of Flight Simulatoe Motion Cues Using Extended Kalman Filtering. Proceedings of AIAA Modeling and Simulation Techonologies Conference and Exhibit. Honolulu, Hawaii, AIAA-2008-6539
104 J. A. Lawton, R. J. Jesionowaki and P. Zarchan. Comparison of Four Filtering Options for Radar Tracking Problem. Journal of Guidance, Control, and Dynamics. 1998, 21(4):618-623
105 M. Psiaki. Kalman Filtering and Smoothing to Estimate Real-Valued States and Integer Constants. Proceedings of AIAA Guidance, Navigation and Control Conference. Chicago, IIIinois, AIAA-2009-5972
106 E. J. Leffert, F. L. Markley and M. D. Shuster. Kalman Filtering forSpacecraft Attitude Estimation. Journal of Guidance, Control, and Dynamics. 1982, 5(5):417-429
107 S. F. Schmidt. The Kalman Filter-Its Recognition and Development for Aerospace Applications. Journal of Guidance, Control, and Dynamics. 1981, 4(1):4-7
108 D. Jung and P. Tsiotras. Inertial Attitude and Position Reference System Development for a Small UAV. Proceedings of Infotech Aerospace 2007 Conference and Exhibit. Rohnert Park, California, AIAA-2007-763
109程杨.基于星敏感器的空间飞行器姿态和姿态角速度确定.哈尔滨工业大学博士论文. 2003:5-19
110 S. J. Julier, J. K. Uhlmann and H. F. Durrant-Whyte. A New Approach for Filtering Nonlinear System. Proceedings of the American Control Conference. Seattle, WA, 1995:1628-1632
111 S. J. Julier, J. K. Uhlmann and H. F. Durrant-Whyte. A New Method for Nonlinear Transformation of Means and Covariances in Filters and Estimators. IEEE Transaction on Automatic Control. 2000, 45(3):477-482
112潘泉,杨峰,叶亮,梁彦,程咏梅.一类非线性滤波器——UKF综述.控制与决策. 2005, 20(5):481-494
113 K. Ito and K. Xiong. Gaussian Filters for Nonlinear Filter Problems. IEEE Transactions on Automatic Control. 2000, 45(5):910-927
114 M. Norgaard, N. Poulsen and O. Ravn. New Developments in State Estimation for Nonlinear Systems. Automatic. 2000, 36(11):1627-1638
115 R. V. D. Merwe. Sigma-Point Kalman Filters for Probabilistic Inference in Dynamics State-Space Models. Ph.D Dissertion of Oregon Health and Science University, 2004:49-126
116 S. Oh and E. Johnson. Relative Motion Estimation for Vision-Based Formation Flight Using Unscented Kalman Filter. Proceedings of AIAA Guidance, Navigation and Control Conference and Exhibit. Hilton Head, South Carolina, AIAA-2007-6866
117 K. Roh, S. Park and K. Choi. Orbit Determination Using the Geomagnetic Field Measurement via the Unscented Kalman Filter. Journal of Spacecraft and Rockets. 2007, 44(1):246-253
118 N. Stastny, R. Bettinger and F. Chavez. Comparison of the Extended andUnscented Kalman Filters for Angles Based Relative Navigation. Proceedings of AIAA/AAS Astrodynamics Specialist Conference and Exhibit. Honolulu, Hawaii, AIAA-2008-7083
119 J. Fisher and S. Vadali. Gyroless Attitude Control of Mutibody Satellites Using an Unscented Kalman Filter. Journal of Guidance, Control, and Dynamics. 2008, 31(1):245-251
120 L. Perea and P. Elosegui. New State Update Equation for the Unscented Kalman Filter. Journal of Guidance, Control, and Dynamics. 2008, 31(5):1500-1504
121 A. P. Sage and G. W. Husa. Adaptive Filtering with Unknown Prior Statistics. Proceedings of Joint Automatic Control Conference. Boulder, CO, 1969:760-769
122 T. Yoshimura and T. Sueda. A technique for Compenating the Filter Performance by a Fictitious Noise. Transaction of the ASME. 1978, 100(2):154-156
123 B. Fridland. Treatment of Bias in Recursive Filtering. IEEE Transaction on Automatic Control. 1969, 14(4):359-367
124 P. P. Khargonekar and M. A. Rotea. Mixed H 2 /H∞Filtering. Proceedings of the 31th Conference on Decision and Control. Tucson, Arizona, 1992, 2:2299-2304
125 D. S. Berstein and W. M. Haddad. Steady-State Filtering with an H∞Error Bound. System & Control Letters. 1989, 12:9-16
126 W. M. Haddad, D. S. Berstain and D. Mustafa. Mixed-Norm H 2 /H∞Regulation and Estimation: the Discrete-Time Case. System & Control Letters. 1991, 16:235-247
127 P. J. Huber. Robust Estimation of a Location Parameter. Annuals of Mathematical Statistics. 1964, 35(2):73-101
128 P. Patrus. Robust Huber Adaptive Filter. IEEE Transaction on Signal Processing. 1999, 47(4):1129-1133
129 C. D. Karlgaard. Robust Rendezvous Navigation in Elliptical Orbit. Journal of Guidance, Control, and Dynamics. 2006, 29(2):495-499
130 C. D. Karlgaard and H. Schaub. Huber-Based Divided Difference Filtering. Journal of Guidance, Control, and Dynamics. 2007, 30(3):885891
131 C. D. Karlgaard and H. Schaub. Adaptive Huber-Based Filtering Using Projection Statistics: Application to Spacecraft Attitude Estimation. Proceedings of AIAA Guidance, Navigation and Control Conference and Exhibit. Honolulu, Hawaii, AIAA-2008-7389
132孙书利.最优与自适应信息融合卡尔曼估计器及其应用.哈尔滨工业大学博士论文. 2004:5-10
133 D. Willner, C. B. Chang and K. P. Dunn. Kalman Filter Algorithm for a Multisensor System. Proceedings of IEEE Decision and Control Conference. 1976:570-574
134 M. F. Hassan, G. Salut and M. Singh. A Decetralized Computational Algorithm for the Global Kalman Filter. IEEE Transaction on Automatic Control. 1978, 23(2):262-268
135 T. H. Kerr. Decentralized Filtering and Redundancy Management. IEEE Transaction on Aerospace and Electronic Systems. 1987, 23(6):83-119
136 J. Manyika and H. F. Durrant-Whyte. Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach. Ellis Horwood, 1994:33-47
137 G. Girija and J. Raol. Application of Information Filter for Sensor Data Fusion. Proceedings of 38th Aerospace Sciences Meeting and Exhibit. Reno, NV, AIAA-2000-0894
138 D. Lee. Unscented Information Filtering for Distributed Estimation and Multiple Sensor Fusion. Proceedings of AIAA Guidance, Navigation and Control Conference and Exhibit. Honolulu, Hawaii, AIAA-2008-7426
139 D. Sun and J. L. Crassidis. Observability Analysis of Six-degree-of-freedom Configuration Determination using Line-of-sight. Journal of Guidance, Control, and Dynamics. 2002, 25(6):1149-1157