-
摘要:
针对机载光电吊舱受迫振动和框架角动态变化导致的动态弹性杆臂效应问题，提出一种补偿弹性多杆臂误差的组合导航滤波方法。通过分析吊舱受迫振动的主要形式，建立吊舱线振动和角振动的动力学模型及由吊舱振动导致的弹性外杆臂模型；在多杆臂误差模型的基础上，对吊舱振动导致的速度、位置误差及振动角误差进行推导，分别建立弹性外杆臂误差模型和振动角误差模型，并将其引入状态向量中，通过对不同机动状态下的系统状态向量进行可观测度分析，得到各状态向量可观测的充分条件，为规划机动方案提供了基于可观测度的自适应调整依据。仿真实验结果表明：相较于刚性杆臂误差补偿，弹性多杆臂误差补偿后的速度误差均方根误差最大减少了73.4%，位置误差均方根误差最大减少了77.4%，有效提升了吊舱组合导航精度。
Abstract:Aiming at the problem of the dynamic elastic lever arm effect caused by the forced vibration of the airborne photoelectric pod and the dynamic change of frame angle, an integrated navigation filtering method for online compensation of elastic multi lever arm error is proposed. Firstly, the dynamic model of the linear and angular vibrations of the pod as well as the elastic outer arm model induced by the vibration of the pod are established by examining the primary forms of the forced vibration of the pod. Secondly, based on the multi-arm error model, the velocity, position error and vibration angle error caused by the vibration of the pod are deduced. The elastic outer arm error model and the vibration angle error model are established and introduced into the state vector. The required requirements for each state vector’s observability are found by examining the observability of the system state vectors under various maneuvering states. This provides an adaptive adjustment foundation based on the observability for the planning maneuver scheme. The simulation results show that the root mean square error of the velocity error is reduced by 73.4% and the root mean square error of the position error is reduced by 77.4% after the elastic multi-arm error compensation, compared with the rigid arm error compensation, which effectively improves the accuracy of the pod integrated navigation.
-
Key words:
- photoelectric pod /
- forced vibration /
- lever arm error /
- Kalman filtering /
- observability analysis
-
图 7 不同机动条件下杆臂相关状态向量的可观测度
Figure 7. The observable degree of the arm-related state vector under different maneuvering conditions
图 9 仿真二维飞行轨迹局部放大图
Figure 9. Simulation of two-dimensional flight trajectory local amplification diagram
表 1 状态变量对应的奇异值分析结果
Table 1. Singular value analysis results corresponding to state variables
机动方式 奇异值 $ {\phi }_{\text{E}} $ $ {\phi }_{\text{N}} $ $ {\phi }_{\text{U}} $ $ \delta {v}_{\text{E}} $ $ \delta {v}_{\text{N}} $ $ \delta {v}_{\text{U}} $ $ \delta L $ $ \delta \lambda $ $ \delta h $ $ {\varepsilon }_{x} $ $ {\varepsilon }_{y} $ $ {\varepsilon }_{\textit{z}} $ 1 26.81 26.81 $ 4.38\times {10}^{-4} $ 4.73 4.73 8.42 13.41 13.41 13.41 33.62 33.62 $ 2.11\times {10}^{-4} $ 2 39.69 39.69 $ 2.23\times {10}^{-3} $ 6.62 6.62 17.74 22.31 22.31 22.31 40.09 40.09 $ 2.46\times {10}^{-3} $ 3 47.73 47.73 21.17 6.62 6.62 17.74 22.31 22.31 22.31 50.29 50.29 5.92 4 57.45 57.45 15.83 6.62 6.62 17.74 22.31 22.31 22.31 48.11 48.11 4.59 5 65.78 65.78 20.68 6.62 6.62 17.74 22.31 22.31 22.31 59.17 59.17 8.76 机动方式 奇异值 $ {\nabla }_{x} $ $ {\nabla }_{y} $ $ {\nabla }_{\textit{z}} $ $ \delta \boldsymbol{l}_{\text{sd}x}^{\text{s}} $ $ \delta \boldsymbol{l}_{\text{sd}y}^{\text{s}} $ $ \delta \boldsymbol{l}_{\text{sd}z}^{\text{s}} $ $ \delta \boldsymbol{l}_{\text{dg}x}^{\text{d0}} $ $ \delta \boldsymbol{l}_{\text{dg}y}^{\text{d0}} $ $ \delta \boldsymbol{l}_{\text{dg}z}^{\text{d0}} $ $ \boldsymbol{z} $ $ {\gamma }_{\text{dr}} $ $ {\theta }_{\text{dr}} $ 1 $ 2.30\times {10}^{-4} $ $ 2.30\times {10}^{-4} $ 4.57 $ 4.01\times {10}^{-5} $ $ 4.01\times {10}^{-5} $ $ 4.01\times {10}^{-5} $ $ 4.33\times {10}^{-5} $ $ 4.33\times {10}^{-5} $ $ 4.33\times {10}^{-5} $ $ 4.33\times {10}^{-5} $ $ 3.72\times {10}^{-5} $ $ 3.72\times {10}^{-5} $ 2 $ 1.97\times {10}^{-3} $ $ 1.97\times {10}^{-3} $ 7.31 $ 0.93\times {10}^{-4} $ $ 0.93\times {10}^{-4} $ $ 0.93\times {10}^{-4} $ $ 1.22\times {10}^{-4} $ $ 1.22\times {10}^{-4} $ $ 1.22\times {10}^{-4} $ $ 1.22\times {10}^{-4} $ 1.14 1.14 3 1.44 1.44 2.16 1.06 1.06 1.27 $ 1.22\times {10}^{-4} $ $ 1.22\times {10}^{-4} $ $ 1.22\times {10}^{-4} $ $ 1.22\times {10}^{-4} $ 1.55 1.55 4 1.21 1.21 1.49 $ 2.37\times {10}^{-3} $ $ 2.37\times {10}^{-3} $ $ 2.37\times {10}^{-3} $ 0.94 0.94 0.94 0.94 1.36 1.36 5 2.65 2.65 3.16 1.89 1.89 1.89 1.27 1.27 1.27 1.27 1.93 1.93 
下载: 导出CSV
表 2 速度误差均方根误差
Table 2. The root mean square error of velocity error
数据类别 速度误差均方根误差/(m·s−1) 东向 北向 天向 刚性补偿后 0.014 8 0.015 8 0.014 7 弹性补偿后 0.004 8 0.005 9 0.003 9
下载: 导出CSV
表 3 位置误差均方根误差
Table 3. The root mean square error of position error
数据类别 位置误差均方根误差/m 东向 北向 天向 刚性补偿后 0.038 9 0.085 8 0.050 0 弹性补偿后 0.012 8 0.043 0 0.011 3
下载: 导出CSV
-
[1] 梁卫清, 魏志强, 袁红伟, 等. 小型高性能无人机载光电吊舱的发展现状与方向[J]. 电视技术, 2022, 46(7): 65-68.LIANG W Q, WEI Z Q, YUAN H W, et al. Development status and direction of small-scale high-performance UAV on-board optoelectronic pod[J]. Video Engineering, 2022, 46(7): 65-68(in Chinese). [2] 高卓, 江泽, 邓麟. 机载光电吊舱目标定位技术研究[J]. 导航定位学报, 2013, 1(4): 74-78.GAO Z, JIANG Z, DENG L. Research on airborne optoelectronic pod target location method[J]. Journal of Navigation and Positioning, 2013, 1(4): 74-78(in Chinese). [3] 甄云卉, 路平. 无人机相关技术与发展趋势[J]. 兵工自动化, 2009, 28(1): 14-16.ZHEN Y H, LU P. Related technologies and development of UAV[J]. Ordnance Industry Automation, 2009, 28(1): 14-16(in C-hinese). [4] 杨小康, 严恭敏, 崔涛, 等. 用于机载光电吊舱的MEMS-INS/GNS-S组合导航系统杆臂误差精确建模与补偿方法[C]//中国惯性技术学会2021科技工作者研讨会论文集——前沿技术与惯性技术的融合与应用论文集. 大连: 中国惯性技术学会, 2021: 125-131.YANG X K, YAN G M, CUI T, et al. Accurate modeling and compensation method of lever arm error of MEMS-INS/GNSS integrated navigation system for airborne photoelectric pod[C]//Proceedings of the Symposium on Dynamic Development Direction of Inertial Technology Development-Integration and Application of Frontier Technology and Inertial Technology. Dalian: Chinese Society of Inertial Technology, 2021: 125-131(in Chinese). [5] 张兰霜. 某型光电吊舱的隔振研究[D]. 哈尔滨: 哈尔滨工业大学, 2021: 10-23.ZHANG L S. Research on vibration isolation of a photoelectric pod[D]. Harbin: Harbin Institute of Technology, 2021: 10-23( in Chinese). [6] CHEN J Y, ZHANG D L, HAN G T, et al. A method for lever arm estimation in INS/GPS integration using direct unscented Kalman filter[C]//Proceedings of the 2020 IEEE 6th International Conference on Computer and Communications. Piscataway: IEEE Press, 2020: 985-990. [7] STOVNER B N, JOHANSEN T A. GNSS-antenna lever arm compensation in aided inertial navigation of UAVs[C]//Proceedings of the 2019 18th European Control Conference. Piscataway: IEEE Press, 2019: 4040-4046. [8] HONG S, LEE M H, CHUN H H, et al. Experimental study on the estimation of lever arm in GPS/INS[J]. IEEE Transactions on Vehicular Technology, 2006, 55(2): 431-448. [9] 徐景硕, 王勇军, 刘亚. 大失准角情形下UKF与CKF的比较研究[J]. 电光与控制, 2017, 24(9): 42-46.XU J S, WANG Y J, LIU Y. Comparative study on UKF and CKF in large azimuth misalignment for SINS[J]. Electronics Optics & Control, 2017, 24(9): 42-46 (in Chinese). [10] CONG M Y, LU H, CHENG X, et al. Transfer alignment method and realization of SINS on moving base based on Kalman filter[C]//Proceedings of the 2019 IEEE 1st International Conference on Civil Aviation Safety and Information Technology. Piscataway: IEEE Press, 2020: 329-333. [11] WANG B, YE W, LIU Y. An improved real-time transfer align-ment algorithm based on adaptive noise estimation for distributed POS[J]. IEEE Access, 2020, 8: 102119-102127. [12] CAO Q, ZHONG M, ZHAO Y. Dynamic lever arm compensation of SINS/GPS integrated system for aerial mapping[J]. Measurement, 2015, 60: 39-49. [13] 翁浚, 刘健宁, 寇科, 等. 吊舱SINS/GNSS组合导航多杆臂效应在线估计算法[J]. 中国惯性技术学报, 2021, 29(2): 184-190.WENG J, LIU J N, KOU K, et al. Online estimation algorithm of multi-lever arm effect for integrated pod SINS/GNSS navigation[J]. Journal of Chinese Inertial Technology, 2021, 29(2): 184-190(in Chinese). [14] 徐晓苏, 邹海军, 刘义亭, 等. 基于鲁棒滤波的挠曲变形和动态杆臂补偿算法[J]. 中国惯性技术学报, 2015(1): 9-13.XU X S, ZOU H J, LIU Y T, et al. Compensation algorithm of flexural deformation and dynamic lever-arm based on robust filtering[J]. Journal of Chinese Inertial of Technology, 2015(1): 9-13(in Chinese). [15] GAO Q, ZHAO G, WANG X. Transfer alignment error com-pensator design for flexure and lever-arm effect[C]//Proceedings of the IEEE Conference on Industrial Electronics and Applications. Xi’an: ICIEA, 2009. [16] CAO Q, ZHONG M, GUO J. Non-linear estimation of the flexural lever arm for transfer alignment of airborne distributed position and orientation system[J]. IET Radar, Sonar & Navigation, 2017, 11(1): 41-51. [17] 谷雨, 司帆, 赵剡, 等. 一种改进的机载武器传递对准中杆臂效应动态补偿方法[J]. 弹箭与制导学报, 2018, 38(1): 41-44.GU Y, SI F, ZHAO Y, et al. An improved dynamic compensation method for lever arm effect in transfer alignment of airborne weapon[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2018, 38(1): 41-44(in Chinese). [18] 严恭敏, 翁浚. 捷联惯导算法与组合导航原理[M]. 西安: 西北工业大学出版社, 2019: 204-207.YAN G M, WENG J. Strapdown inertial navigation algorithm and integrated navigation principle[M]. Xi’an: Northwestern Polytechnical University Press, 2019: 204-207(in Chinese). -
下载:
点击查看大图
计量
- 文章访问数: 289
- HTML全文浏览量: 122
- PDF下载量: 7
- 被引次数: 0
