基于声学测量的水下高动态航行参数估计

doi:10.12382/bgxb.2024.0741

摘要/Abstract

摘要：

针对水下高动态航行体运动特征难以识别的问题,提出一种基于水声测量的运动参数序列估计方法。该方法通过逐点解算得到位置、速度序列,再进行函数化重构和参数辨识,消除随机误差引起的数据抖动,实现平稳、连续的序贯参数获取。在仿真分析中,模拟了边长为数百米的海底四元阵对数十米深度垂向航行过程的测量,参数估计结果的垂向精度相对于逐点解算结果明显提升,位置和速度均方根误差(Root Mean Square Error,RMSE)分别减小27.8%和47.2%。经大样本计算,参数序列估计值趋近于真值,垂向位置和速度偏差分别为0.042m和0.056m/s。水池试验结果表明,物理模型的高动态航行参数估计结果与惯性传感器数据相吻合,垂向位置和速度RMSE分别为0.024m和0.141m/s。新方法可为水下高动态航行体运动性能检验提供准确、平滑、连续的参数估计结果,在海上测试中有一定工程应用前景。

关键词: 水下高动态航行体, 多项式函数, 运动参数估计, 水声测量

Abstract:

A motion parameter estimation method based on acoustic measurement is proposed for identifying the kinetic characteristics of high dynamic underwater vehicle. In the proposed method, the position and velocity data series are obtained by point-by-point solution, and then the data jitters caused by random errors are removed by functional reconstruction and coefficient identification, so a smooth and continuous parameter sequence is obtained. In simulation, the high dynamic underwater vehicle moves from the depth of several tens of meters to surface, and the motion parameter is measured by the seabed four-receiver array. The simulated results show that the estimated parameter sequence has higher accuracy in vertical direction than the point-by-point solved one, and the root mean square errors (RMSE) of position and velocity are reduced by 27.8% and 47.2%, respectively. Calculations based on large samples indicate that the estimated parameter sequence approaches the true value, and the deviations of position and velocity are 0.042m and 0.056m/s, respectively. The water-tank test results show that the estimated parameter sequence generated by the high dynamic physical model is well consistent with the inertial sensor data, and the RMSEs of position and velocity in vertical direction are 0.024m and 0.141m/s, respectively. The proposed method can provide an accurate, smooth and continuous parameter sequence for the high dynamic underwater vehicle in performance testing and has certain engineering application value in sea trials.

Key words: high dynamic underwater vehicle, polynomial function, motion parameter estimation, underwater acoustic measurement

中图分类号:

TJ06

张旭. 基于声学测量的水下高动态航行参数估计[J]. 兵工学报, 2025, 46(5): 240741-.

ZHANG Xu. Motion Parameter Estimation of High Dynamic Underwater Vehicle Based on Acoustic Measurement[J]. Acta Armamentarii, 2025, 46(5): 240741-.

图/表 11

图1 水下高动态航行体运动参数测量示意图

Fig.1 Schematic diagram of motion parameter measurement for the high dynamic underwater vehicle

表1 模拟航行轨迹的多项式函数系数

Table 1 Polynomial function parameters of the simulated motion trajectory

x轴方向参数				y轴方向参数				z轴方向参数
a₁	b₁	c₁	d₁	a₂	b₂	c₂	d₂	a₃	b₃	c₃	d₃
4.00	-8.00	1.00	-0.50	-0.17	0.50	-0.01	0.00	-21.67	80.00	-60.00	-20.00

图2 模拟的航行轨迹及参数序列

Fig.2 Simulated motion trajectory and parameter sequence

图3 航行轨迹与测站位置分布图

Fig.3 Schematic diagram of target trajectory and observational stations

图4 单次观测的各测站测量要素曲线

Fig.4 Curves of measuring element in each observational station under once observation condition

图5 单次观测的航行轨迹及参数序列

Fig.5 Motion trajectory and parameter sequence under once observation condition

图6 斜距MSE迭代箱形图

Fig.6 MSE box plot of slant range

图7 大样本参数序列的RMSE曲线

Fig.7 RMSE curves of parameter sequence based on large samples

表2 基于大样本系数辨识的多项式函数系数

Table 2 Polynomial function parameters obtained by coefficient identification based on large samples

x轴方向参数				y轴方向参数				z轴方向参数
a₁	b₁	c₁	d₁	a₂	b₂	c₂	d₂	a₃	b₃	c₃	-d₃
4.00	-7.99	0.99	-0.49	-0.17	0.51	-0.03	0.00	-21.73	80.19	60.11	-20.05

图8 水池试验航行轨迹与测站位置分布图

Fig.8 Schematic diagram of motion trajectory and observational stations in the water-tank test

图9 水池试验的航行轨迹及参数序列

Fig.9 Motion trajectory and parameter sequence in the water-tank test

参考文献 31

[1]	LU J X, WANG C, SONG W C, et al. Experimental investigation on interference characteristics of projectiles launched successively underwater[J]. Ocean Engineering, 2022, 250: 110824.
[2]	施瑶, 任锦毅, 高山, 等. 航行体水下连续发射尾涡遭遇与运动干扰特性[J]. 兵工学报, 2023, 44(10): 3195-3203. doi: 10.12382/bgxb.2022.0616
	SHI Y, REN J Y, GAO S, et al. A study on the wake vortex encounter and movement interference characteristics of projectiles successively launched underwater[J]. Acta Armamentarii, 2023, 44(10): 3195-3203. (in Chinese) doi: 10.12382/bgxb.2022.0616
[3]	刘方, 肖金石, 韦建明, 等. 水下连续发射弹体干扰特性及发射时序优化[J]. 兵工学报, 2024, 45(1): 197-205. doi: 10.12382/bgxb.2022.0576
	LIU F, XIAO J S, WEI J M, et al. Interference characteristics and launch sequence optimization of projectiles launched successively underwater[J]. Acta Armamentarii, 2024, 45(1): 197-205. (in Chinese) doi: 10.12382/bgxb.2022.0576
[4]	SHI Y, WANG G H, PAN G. Experimental study on cavity dynamics of projectile water entry with different physical parameters[J]. Physics of Fluids, 2019, 31(6): 067103.
[5]	杨晓光, 党建军, 王鹏, 等. 波浪对航行体高速入水载荷特性影响[J]. 兵工学报, 2022, 43(2): 355-362.
	YANG X G, DANG J J, WANG P, et al. The influence of waves on the impact load during high-speed water-entry of a vehicle[J]. Acta Armamentarii, 2022, 43(2): 355-362. (in Chinese) doi: 10.3969/j.issn.1000-1093.2022.02.013
[6]	MA G H, CHEN F, YU J Y, et al. Numerical investigation of trajectory and attitude robustness of an underwater vehicle considering the uncertainty of platform velocity and yaw angle[J]. Journal of Fluids Engineering, 2019, 141(2): 021106.
[7]	姚熊亮, 赵斌, 马贵辉. 跨介质航行体出水问题研究现状与展望[J]. 航空学报, 2024, 45(14): 029598.
	YAO X L, ZHAO B, MA G H. Research status and prospect of cross-media vehicle water-exit problem[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(14): 029598. (in Chinese)
[8]	张旭, 李万鹏. 跨介质出水航行器水下信息获取技术发展与展望[J]. 水下无人系统学报, 2024, 32(4): 659-667.
	ZHANG X, LI W P. Progress and prospect of underwater information acquisition technique for trans-medium vehicles during water exit[J]. Journal of Unmanned Undersea Systems, 2024, 32(4): 659-667. (in Chinese)
[9]	孙大军, 侯开阳, 滕婷婷, 等. 空时多普勒频移域运动小目标的抗干扰探测方法[J]. 声学学报, 2022, 47(2): 161-174.
	SUN D J, HOU K Y, TENG T T, et al. Small moving target interference suppression detection method in space-time Doppler frequency shift domain[J]. Acta Acustica, 2022, 47(2): 161-174. (in Chinese)
[10]	刘方, 侯超强, 翟涛涛, 等. 运动声源多普勒畸变信号自适应校正方法[J]. 声学学报, 2022, 47(6): 820-831.
	LIU F, HOU C Q, ZHAI T T, et al. An adaptive correction mehtod for Doppler distorted signal of moving sound source[J]. Acta Acustica, 2022, 47(6): 820-831. (in Chinese)
[11]	孙思博, 明瑞和, 杨卓, 等. 针对周期偏移声信标的二阶高精度水声定位模型[J]. 声学学报, 2023, 48(1): 41-49.
	SUN S B, MING R H, YANG Z, et al. The second order model for underwater acoustic location of periodic sources[J]. Acta Acustica, 2023, 48(1): 41-49. (in Chinese)
[12]	徐文, 鄢社锋, 季飞, 等. 海洋信息获取、传输、处理及融合前沿研究评述[J]. 中国科学: 信息科学, 2016, 46(8): 1053-1085.
	XU W, YAN S F, JI F, et al. Marine information gathering, transmission, processing, and fusion: current status and future trends[J]. Scientia Sinica(Informationis), 2016, 46(8): 1053-1085. (in Chinese)
[13]	HUANG J, YAN S G. An improvement of long baseline system using particle swarm optimization to optimize effective sound speed[J]. Marine Geodesy, 2018, 41(5): 439-456.
[14]	张旭, 孙翱, 韩旭, 等. 水下垂向运动目标的海底多基站声定位方法及精度分析[J]. 声学学报, 2019, 44(2): 155-169.
	ZHANG X, SUN A, HAN X, et al. Acoustic localization scheme and accuracy analysis for underwater vertical motion target using multi-stations in the seabed[J]. Acta Acustica, 2019, 44(2): 155-169. (in Chinese)
[15]	ZHANG J C, HAN Y F, ZHENG C E, et al. Underwater target localization using long baseline positioning system[J]. Applied Acoustics, 2016, 111: 129-134.
[16]	QI K, QU G Q, XUE S Q, et al. Analytical optimization on GNSS buoy array for underwater positioning[J]. Acta Oceanologica Sinica, 2019, 38(7): 137-143.
[17]	JIA T Y, SHEN X H, WANG H Y. Multistatic sonar localization with a transmitter[J]. IEEE Access, 2019, 7: 111192-111203. doi: 10.1109/ACCESS.2019.2934737
[18]	孙大军, 郑翠娥, 张居成, 等. 水声定位导航技术的发展与展望[J]. 中国科学院院刊, 2019, 34(3): 331-338.
	SUN D J, ZHENG C E, ZHANG J C, et al. Development and prospect for underwater acoustic positioning and navigation technology[J]. Bulletin of Chinese Academy of Sciences, 2019, 34(3): 331-338. (in Chinese)
[19]	YAN W S, CHEN W, CUI R X. Moving long baseline positioning algorithm with uncertain sound speed[J]. Journal of Mechanical Science and Technology, 2015, 29(9): 3995-4002.
[20]	LIU Y X, LU X S, XUE S Q, et al. A new underwater positioning model based on average sound speed[J]. Journal of Navigation, 2021, 74(5): 1009-1025.
[21]	贾天一, 高婧洁, 申晓红, 等. 声速不确定条件下的运动水下航行器自定位[J]. 系统工程与电子技术, 2022, 44(9): 2699-2706. doi: 10.12305/j.issn.1001-506X.2022.09.01
	JIA T Y, GAO J J, SHEN X H, et al. Moving underwater vehicle localization with uncertain sound speed[J]. Systems Engineering and Electronics, 2022, 44(9): 2699-2706. (in Chinese) doi: 10.12305/j.issn.1001-506X.2022.09.01
[22]	马顺南, 路宇, 王炯琦, 等. 基于组合圆锥构型的水下声定学定位系统布站优化[J]. 弹道学报, 2024, 36(2): 10-20.
	MA S N, LU Y, WANG J Q, et al. Station layout optimization for underwater acoustic positioning system based on combined-cone configuration[J]. Journal of Ballistics, 2024, 36(2): 10-20. (in Chinese)
[23]	JIA T Y, HO K C, WANG H Y et al. Effect of sensor motion on time delay and Doppler shift localization: analysis and solution[J]. IEEE Transactions on Signal Processing, 2019, 67(22): 5881-5895.
[24]	孙旭, 李然威, 周利生, 等. 利用组合双曲调频信号的目标径向速度测量与测距去偏[J]. 声学学报, 2024, 49(5): 979-989.
	SUN X, LI R W, ZHOU L S, et al. Radial velocity and range bias estimation using composite hyperbolic frequency-modulated signals[J]. Acta Acustica, 2024, 49(5): 979-989. (in Chinese)
[25]	GONG Z J, LI C, JIANG F. AUV-aided joint localization and time synchronization for underwater acoustic sensor networks[J]. IEEE Signal Processing Letters, 2018, 25(4): 477-481.
[26]	ZHANG J C, SHI C H, SUN D J, et al. High-precision, limited-beacon-aided AUV localization algorithm[J]. Ocean Engineering, 2018, 149:106-112.
[27]	杨元喜, 刘焱雄, 孙大军, 等. 海底大地基准网建设及其关键技术[J]. 中国科学: 地球科学, 2020, 50(7):1-10.
	YANG Y X, LIU Y X, SUN D J, et al. Seafloor geodetic network establishment and key technologies[J]. Scientia Sinica (Terrae), 2020, 50(7):1-10. (in Chinese)
[28]	孙大军, 欧阳雨洁, 韩云峰, 等. 海底大地秩亏基准网快速标校方法[J]. 声学学报, 2023, 48(3): 506-514.
	SUN D J, OUYANG Y J, HAN Y F, et al. Rapid calibration method for rank defect submarine geodetic station network[J]. Acta Acustica, 2023, 48(3): 506-514. (in Chinese)
[29]	冯旭东, 邢尧, 王炯琦, 等. 基于最优模型选择的多信标长基线定位系统误差辨识方法[J]. 弹道学报, 2024, 36(1): 85-96.
	FENG X D, XING Y, WANG J Q, et al. Systematic error identification method of multi-beacon long baseline positioning based on optimal model selection[J]. Journal of Ballistics, 2024, 36(1): 85-96. (in Chinese)
[30]	张瑜, 吴凯, 郭杰, 等. 基于数据质量评估的自适应序贯航迹关联算法[J]. 系统工程与电子技术, 2022, 44(11): 3477-3485. doi: 10.12305/j.issn.1001-506X.2022.11.23
	ZHANG Y, WU K, GUO J, et al. Adaptive sequential track-association algorithm based on data quality assessment[J]. Systems Engineering and Electronics, 2022, 44(11): 3477-3485. (in Chinese) doi: 10.12305/j.issn.1001-506X.2022.11.23
[31]	CUI Y Q, LIU Y, TANG T T, et al. A new adaptive track correlation method for multiple scenarios[J]. IET Radar, Sonar & Navigation, 2021, 15(9): 1112-1124.