Adaptive square-root unscented Kalman filter for position and pose prediction of UAV
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摘要: 针对无人机自主导航的实时性差、精度低且对时变噪声的鲁棒性弱的问题，建立了机器视觉和惯性导航相融合的组合导航系统，并提出了一种自适应平方根无迹卡尔曼滤波(adaptive square-root unscented kalman filter, ASRUKF)算法。该算法通过观测值与估计值残差的Mahalanobis距离时刻修正系统噪声协方差，再与采用最小偏度采样的SRUKF算法相融合，从而达到时变噪声自适应抑制，滤波快速且对噪声鲁棒性高的效果。仿真结果表明，相比标准SRUKF，ASRUKF计算耗时减少约38.8%，位移、速度和姿态角预测精度分别提高超过4倍和6倍，且对于时变噪声鲁棒性更强。
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关键词:
- 自主空中加油 /
- 噪声自适应 /
- Mahalanobis距离 /
- 最小偏度采样 /
- 平方根无迹卡尔曼滤波
Abstract: To improve the real-time performance, the accuracy and the robustness of the unmanned aerial vehicle (UAV) autonomous navigation system under time-varying noise circumstance, an integrated navigation system of machine vision and inertial guidance was established, and an adaptive square-root unscented Kalamn filter (ASRUKF) was proposed. By introducing the Mahalanobis distance of the residual between the observed and predicted values to the minimal skew sampling square-root Kalman filter, the new algorithm can restrain the system noise adaptively, compute faster and be more robust to noise. The simulation results show that compared with the SRUKF, the ASRUKF is more robust to noise, and the computation time is reduced by about 38.8%, the forecast accuracy of displacement, velocity and attitude angle increases by more than 4 times, 4times, 6 times, respectively. -
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表 2 滤波参数设置
Table 2 Filter parameters setting
参数 符号 数值 样本时间 T 0.1/s 仿真时间 SimTime 130/s 标志点个数 N 10/个 相机焦距 F 555/mm 图像中心 (Ox, Oy) (325, 174) 初始方差阵 P0 (50/3)2×I9×9 量测误差 Rk I9×9 系统误差 Qk [1+1.2cos(t)]×I9×9 尺度因子 α
β
κ0.120 
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表 3 算法单步耗时比较
Table 3 Comparsion of single step running time of algorithm
初始值/s 改进值/s 0.019 920 0.012 182
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[1] NATASSYA B F, SILVA D, DANIEL B W, et al. Performance evaluation of the extended kalman filter and unscented kalman filte[C]//IEEE International Conference on Unmanned Aircraft systems (IUAS), USA: IEEE, 2015.
[2] MARCONATO E A, RODRIGUES D, CHAVES A A, et al. Unmanned aerial vehicles & service-oriented architecture, Larissa and knowledge based framework's first study[C]//The Tenth International Conference on Autonomic and Autonomous Systems (ICAS), USA: IEEE, 2014.
[3] HIRASAWA R, NAKAJIMA Y, MASAKI T. Receding-horizon unscented kalman filter using successive unscented transformation for spacecraft attitude estimation[C]// Florida: AIAA Guidance, Navigation, and Control Conference, AIAA, 2015.
[4] WANG Yidi, SUN Shouming, LI Li, Adaptively robust unscented kalman filter for tracking a maneuvering Vehicle[J]. Journal of Guidance, Control, and Dynamics, 2014, 37(5): 1696-1701. doi: 10.2514/1.G000257
[5] SWEE H T, TAKAAKI K, TORU N, et al. Unscented kalman filter for position estimation of UAV by using image information[C]. USA: SICE Annual Conference, 2015.
[6] 符毅, 孔星炜, 董新民, 等.基于改进UKF的加油机位姿预测方法[J], 应用光学, 2016, 37(6): 860-865. doi: 10.5768/JAO201637.0602004 FU Yi, KONG Xingwei, DONG Xinmin, et al, Tanker pose prediction via modified unscented kalman filter[J]. Journal of Applied Optics, 2016, 37(6):860-865. doi: 10.5768/JAO201637.0602004
[7] 章飞, 周杏鹏, 陈小惠.基于衰减记忆的平方根UKF被动目标跟踪算法[J], 测控技术, 2010, 29(4):22-26. doi: 10.3969/j.issn.1000-8829.2010.04.007 ZHANG Fei, ZHOU Xingpeng, CHEN Xiaohui. Square-Root UKF passive target tracking algorithm based on memory attenuation filtering[J]. Measurement and Control Technique, 2010, 29(4): 22-26. doi: 10.3969/j.issn.1000-8829.2010.04.007
[8] 石勇, 韩崇昭.自适应UKF算法在目标跟踪中的应用[J].自动化学报, 2011, 37(6):755-759. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zdhxb201106012 SHI Yong, HAN Chongzhao. Adaptive UKF method with applications to target tracking[J]. Acta Automatica Sinica, 2011, 37(6): 755-759. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zdhxb201106012
[9] JONATHAN K, DANIEL J K, DIETER F, et al. GP-UKF: unscented kalman filters with gaussian process prediction and observation models[C]//CA, USA San Diego: in the Proceedings of the IEEE/RSJ International Intelligent Robots and Systems San Diego, 2007.
[10] 李鹏, 宋申民, 陈兴林, 自适应平方根无迹卡尔曼滤波算法[J].控制理论与应用, 2010, 27(2): 143-146. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=kzllyyy201002004 LI Peng, SONG Shenmin, CHEN Xinglin, Adaptive square-root unscented Kalman filter algorithm[J]. Control Theory & Application, 2010, 27(2): 143-146. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=kzllyyy201002004
[11] 赵辉, 周欢, 翁兴伟, 等.传感器故障条件下的UKF算法[J].控制与决策, 2015, 30(11): 2025-2032. http://d.old.wanfangdata.com.cn/Periodical/kzyjc201511017 ZHAO Hui, ZHOU Huan, WENG Xingwei, et al. Adaptive UKF algorithm under condition of sensor faults[J]. Control and Decision, 2015, 30(11): 2025-2032. http://d.old.wanfangdata.com.cn/Periodical/kzyjc201511017
[12] 李丹, 刘建业, 熊智, 等.基于最小偏度采样的卫星自主导航SRUKF算法[J].南京航空航天大学学报, 2009, 41(1): 54-58. doi: 10.3969/j.issn.1005-2615.2009.01.011 LI Dan, LIU Jianye, XIONG Zhi, et al. Square root unscented kalman filter for satellite autonomous navigation system based on minimal simplex skew transformation[J]. Journal of NanJing University of Aeronautics & Astronautics, 2009, 41 (1):54-58. doi: 10.3969/j.issn.1005-2615.2009.01.011
[13] JAMSHAID A, RASHEEQ M, UllAH B M. Performance comparison among some nonlinear filters for a low cost SINS/GPS integrated solution[J]. Springer Nonlinear Dyn, 2010, 61(1): 491-502. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=787008f5841435850953884e7416124e
[14] 潘泉, 杨峰, 叶亮, 等.一类非线性滤波器-UKF综述[J].控制与决策, 2005, 20(5): 481-494. doi: 10.3321/j.issn:1001-0920.2005.05.001 PAN Quan, YANG Feng, YE Lang, et al. Survey of a kind of nonlinear Filters-UKF[J]. Control and Decision, 2005, 20(5): 481-494. doi: 10.3321/j.issn:1001-0920.2005.05.001
[15] SONG Qi, HAN Jianda.An adaptive UKF algorithm for the state and parameter of a mobile robot[J]. Acta Automatica Sinica, 2008, 34(1): 72-79.
[16] 孙枫, 唐李军. Cubature卡尔曼滤波器与Unscented卡尔曼滤波估计精度比较[J].控制与决策, 2013, 28(2):303-312. SUN Feng, TANG Lijun. Estimation precision comparison of cubature kalman filter and unscented kalman filter[J]. Control and Decision, 2013, 28(2): 303-312.
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