基于主惯导参数特性的传递对准调平方法

杨管金子; 李建辰; 黄 海; 国琳娜

doi:10.11993/j.issn.2096-3920.2018.06.005

基于主惯导参数特性的传递对准调平方法

doi: 10.11993/j.issn.2096-3920.2018.06.005

中国船舶重工集团公司第705研究所, 陕西西安, 710075

详细信息

作者简介:
杨管金子(1988-), 男, 在读博士, 工程师, 主要研究方向为水下无人系统制导技术.

中图分类号: TJ630.33; U675.7
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- 被引次数: 0
出版历程
- 刊出日期: 2018-12-31

Transfer Alignment and Leveling Method Based on Characteristics of MINS Parameters

The 705 Research Institute, China Shipbuilding Industry Corporation, Xi’an 710075, China

摘要

摘要: 初始对准时间作为一项重要指标, 直接关系到鱼雷在战场上的快速准备性能。传统对准算法及调平判据由于其内部参数一般固定不变, 一方面使得对准时间过长, 另一方面针对不同对准工况的适应性较差。文中提出一种基于主惯导速度注入参数和水平姿态角晃动参数特性的对准调平方法, 采用BP神经网络对该特性进行辨识, 实时自适应对内部特定参数进行在线更新, 在保证对准精度的同时, 以达到较快的滤波器收敛速度和不同工况下的及时调平判定。对比试验结果表明, 该方法能够有效地加快失谐角收敛速度, 且根据不同工况自适应设计调平判据, 可达到缩短初始对准时间的目的。
- 鱼雷 /
- 主惯导参数 /
- 传递对准 /
- 调平判定 /
- BP神经网络
Abstract: The initial alignment time directly relates to the fast prefiring performance of a torpedo. The internal parameters of traditional alignment algorithm and leveling criterion are fixed, which results in longer alignment time and poor adaptation to different alignment conditions. To solve the problem, this study proposed an alignment and leveling method based on the characteristics of main inertial navigation system(MINS) speed injection parameters and horizontal attitude angle sloshing parameter. The back propagation(BP) neural network was used to identify these characteristics, and the internal specific parameters were online adaptively updated in real time. Hence, on the premise of guaranteeing the alignment accuracy, fast convergence rate of the filter and timely leveling judgment in different working conditions could be ensured. Comparative test shows that the proposed method can effectively accelerate the convergence rate of the detuning angle, adaptively design the leveling criterion according to different working conditions, and shorten the initial alignment time.
- torpedo /
- main inertial navigation system(MINS) parameters /
- transfer alignment /
- leveling judgment /
- back propagation(BP) neural network

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参考文献(11)

[1]	骆斐, 尤太华, 刘运鹏, 等. 扰动基座下捷联惯组初始对准[J]. 战术导弹技术, 2016(6): 76-79. Luo Fei, You Tai-hua, Liu Yun-peng. et al. Initial Alignment of SIMU with Disturbances[J]. Tactical Missile Technology, 2016(6): 76-79.
[2]	郜福全, 陈丽容, 丁传红, 等. UD分解自适应滤波在SINS初始对准中的应用[J]. 计算机工程与设计, 2017, 35(1): 158-162. Gao Fu-quan, Chen Li-rong, Ding Chuan-hong. et al. Initial Alignment of SINS Based on UD Factorized Adaptive Filter[J]. Computer Egnineering and Design, 2017, 35(1): 158-162.
[3]	苏宛新. 自适应UKF滤波在SINS初始对准中的应用[J]. 中国惯性技术学报, 2011, 19(5): 533-536. Su Wan-xin. Application of Adaptive UKF Filter Technique in Initial Alignment of SINS[J]. Journal of Chinese Inertial Technology, 2011, 19(5): 533-536.
[4]	刘红光, 张崇猛, 陈伟, 等. 舰艇干扰因素对速度匹配传递对准性能的影响[J]. 中国舰船研究, 2013, 8(4): 86-91. Liu Hong-guang, Zhang Chong-meng, Chen Wei, et al. In- terference Factors of Velocity Matching and Transfer Alignment for Shipboard Weapons[J]. Chinese Journal of Ship Research, 2013, 8(4): 86-91.
[5]	Chen Y, Zhao Y. New Rapid Transfer Alignment Method for SINS of Airbome Weapon Systems[J]. Journal of Systems Engineering and Electrics, 2014, 25(2): 281-287.
[6]	Fairfax L D, Fresconi F E. Position Estimation for Projectiles Using Low-cost Sensors and Flight Dynamics[J]. Journal of Aerospace Engineering, 2014, 27(3): 611-620.
[7]	王艳永, 杨功流, 王永苗, 等. 捷联惯性导航系统动态传递对准性能综合评估[J]. 中国惯性技术学报, 2013, 21(4): 425-429. Wang Yan-yong, Yang Gong-liu, Wang Yong-miao, et al. Comprehensive Assessment for Dynamic Transfer Alignment Accuracy of Strap-down Inertial Navigation System[J]. Journal of Chinese Inertial Technology, 2013, 21(4): 425- 429.
[8]	苌永娜, 张海, 李玉洁, 等. 基于R阵动态估计的自适应滤波算法[J]. 电光与控制, 2012, 19(6): 26-31. Chang Yong-na, Zhang Hai, Li Yu-jie, et al. An Adaptive Kalman Filtering Algorithm Based on the Dynamic Estimation of Measurement Noises[J]. Electronics Optics & Control, 2012, 19(6): 26-31.
[9]	章毅, 郭泉, 王建勇. 大数据分析的神经网络方法[J]. 工程科学与技术, 2017, 49(1): 9-18. Zhang Yi, Guo Quan, Wang jian-yong. Big Data Analysis Using Neural Network[J]. Advanced Engineering Sciences, 2017, 49(1): 9-18.
[10]	Silver D, Huang A, Maddison C J, et al. Mastering the Game of Go with Deep Neural Networks and Tree Search[J]. Nature, 2016, 529(7587): 484-489.
[11]	Wang J, Wu W, Zurada J M. Deterministic Convergence of Conjugate Gradient Method for Feedforward Neural Networks[J]. Neurocomputering, 2011, 74(14-15): 2368-2376.