功率谱密度消频散变换被动估计脉冲声源距离

刘建设; 朱广平; 殷敬伟; 陈文剑; 孙辉

doi:10.11999/JEIT231408

功率谱密度消频散变换被动估计脉冲声源距离

doi: 10.11999/JEIT231408

1.
哈尔滨工程大学水声技术全国重点实验室哈尔滨 150001
2.
哈尔滨工程大学极地海洋声学与技术应用教育部重点实验室哈尔滨 150001
3.
哈尔滨工程大学水声工程学院哈尔滨 150001

基金项目: 国家重点研发计划项目 (2021YFC2801200)

详细信息

作者简介:
刘建设：男，博士生，研究方向为水声信号处理

朱广平：男，副教授，研究方向为水声目标探测

殷敬伟：男，教授，研究方向为水声通信

陈文剑：男，副教授，研究方向为水声目标散射

孙辉：男，教授，研究方向为水声物理

通讯作者:
朱广平　guangpingzhu@hrbeu.edu.cn

中图分类号: TN929.3
计量
- 文章访问数: 231
- HTML全文浏览量: 66
- PDF下载量: 41
- 被引次数: 0
出版历程
- 收稿日期: 2023-12-22
- 修回日期: 2024-07-04
- 网络出版日期: 2024-08-02
- 刊出日期: 2024-09-26

Passive Pulse Source Ranging Using De-dispersion Transform of Power Spectral Density

1.
National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China
2.
Key Laboratory for Polar Acoustics and Application of Ministry of Education Ministry of Education, Harbin Engineering University, Harbin 150001, China
3.
College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China

Funds: The National Key Research and Development Program of China (2021YFC2801200)

摘要

摘要: 浅海中传播的低频声波具有多模态特征和频散效应。对接收声信号消频散变换(DDT)可以消除频散效应，实现被动估计声源距离。针对消频散变换存在的测距多值问题，该文提出一种利用功率谱密度消频散变换的被动测距方法(PSD-DDT)。首先使用声场模型KRAKEN计算模态的水平波数；其次在只知道波导不变量大概范围的情况下，估计两个模态之间的频散常数；然后对保留了模态间干涉项的功率谱密度进行消频散变换；最后获得目标距离的估计值为PSD-DDT极大值对应的自变量与频散常数的比值。另外，当海洋参数未知时，需要分别对待测声源和引导声源进行PSD-DDT，利用自变量的比值确定声源距离，这种方法不需要估计频散常数。通过仿真和海试验证了PSD-DDT方法被动测距的有效性，并分析了波导不变量、模态阶数、噪声等因素对距离估计结果的影响。基于黄海试验结果，与DDT方法相比，PSD-DDT的测距误差下降了约49.2%。在35 km范围内最优波导不变量对应的平均相对误差约2.55%，被动测距精度较高。
- 目标探测 /
- 距离估计 /
- 消频散变换 /
- 波导不变量 /
- 功率谱密度
Abstract: The low-frequency sound propagating in the shallow water has the multi-mode characteristic and dispersion effect. The de-dispersion transform of signal frequency spectrum can eliminate the dispersion effect to achieve passive ranging. Focusing on the multi-value problem of the de-dispersion transform of frequency spectrum, a passive ranging method based on the De-Dispersion Transform of Power Spectral Density (PSD-DDT) is proposed. First, the field model KRAKEN is used to calculate the horizontal wavenumbers of each normal mode. Next, given the approximate range of waveguide invariant in the shallow water, the dispersion constant between any two modes is estimated. Then, the power spectral density that retains the modal interference term is subjected to the de-dispersion transform. Finally, the estimated value of the source distance is the ratio of the independent variable corresponding to the maximum amplitude of PSD-DDT to the dispersion constant. In addition, when the waveguide parameters are unknown, PSD-DDT is performed separately on the measured source and the guided source, and the distance is determined by the ratio of the independent variables. This condition does not need to calculate the dispersion constant. The effectiveness of PSD-DDT is verified through numerical simulation and sea trial. The effects of waveguide invariant, mode order, and signal-to-noise ratio on the ranging results are analyzed. Based on the trial data in the Northern Yellow Sea of China, compared with the DDT results, the ranging error of PSD-DDT has decreased by about 49.2%, The relative error within a range of 35 km under the best waveguide invariant is approximately 2.55% with high ranging accuracy.
- Target detection /
- Range estimation /
- De-dispersion transform /
- Waveguide invariant /
- Power spectrum density

HTML全文

图 1 信号仿真

下载: 全尺寸图片幻灯片

图 2 20 km信号的DDT结果

下载: 全尺寸图片幻灯片

图 3 模态频散常数估计

下载: 全尺寸图片幻灯片

图 4 PSD-DDT结果

下载: 全尺寸图片幻灯片

图 5 海上试验概况

下载: 全尺寸图片幻灯片

图 6 引导声源

下载: 全尺寸图片幻灯片

图 7 引导声源的PSD-DDT结果

下载: 全尺寸图片幻灯片

图 8 测距结果对比

下载: 全尺寸图片幻灯片

表 1 仿真参数

	深度(m)	声速(m/s)	密度(g/cm³)	衰减(dB/λ)
海水	60	1500	1.0	0
海底		1700	1.8	0.2

下载: 导出CSV

表 2 波导常数估计结果

m-n阶数	$ \beta $	$ {\gamma _{{mn}}} $	拟合指标R²
1-2	1.136	2.273	0.9997
2-3	1.085	5.186	0.9999
1-2	1.111	2.616	0.9985
2-3	1.111	4.478	0.9998
1-2	1.200	1.646	0.9444
2-3	1.200	2.816	0.9254

下载: 导出CSV

表 3 PSD-DDT测距结果: $ \beta $=1.111

真实距离(km)	r₁₂(km)	误差(%)	r₂₃(km)	误差(%)	测距均值	平均误差(%)
20	19.49	2.53	20.32	1.62	19.91	0.46
40	38.99	2.53	40.64	1.62	39.82	0.46
60	59.24	1.26	60.74	1.25	59.99	0.01
80	79.88	0.14	80.85	1.06	80.37	0.46
100	99.76	0.24	101.84	1.84	100.80	0.80

下载: 导出CSV

表 4 PSD-DDT测距结果: $ \beta $=1.200

真实距离(km)	r₁₂(km)	误差(%)	r₂₃(km)	误差(%)	测距均值	平均误差(%)
20	20.66	3.29	21.66	8.32	21.16	5.80
40	41.92	4.81	43.33	8.32	42.62	6.56
60	62.58	4.31	64.63	7.72	63.61	6.01
80	83.85	4.81	85.94	7.43	84.90	6.12
100	105.12	5.12	106.89	6.89	106.01	6.01

下载: 导出CSV

表 5 测距方法评价

测距方法	MAE(km)	RMSE(km)	MRE(%)
DDT($ \beta $= 1)	3.22	4.36	12.03
PSD-DDT($ \beta $= 1)	1.52	2.10	6.11
PSD-DDT($ \beta $= 1.2)	0.71	1.33	2.55

下载: 导出CSV

参考文献(23)

[1]	FERREIRA B M, GRAÇA P A, ALVES J C, et al. Single receiver underwater localization of an unsynchronized periodic acoustic beacon using synthetic baseline[J]. IEEE Journal of Oceanic Engineering, 2023, 48(4): 1112–1126. doi: 10.1109/JOE.2023.3275611.
[2]	DE MARCO R, DI NARDO F, LUCCHETTI A, et al. The development of a low-cost hydrophone for passive acoustic monitoring of dolphin’s vocalizations[J]. Remote Sensing, 2023, 15(7): 1946. doi: 10.3390/rs15071946.
[3]	LIU Wei, XU Guojun, CHENG Xinghua, et al. A novel finite difference scheme for normal mode models in underwater acoustics[J]. Journal of Marine Science and Engineering, 2023, 11(3): 553. doi: 10.3390/jmse11030553.
[4]	ALTAHER A S, ZHUANG Hanqi, IBRAHIM A K, et al. Detection and localization of goliath grouper using their low-frequency pulse sounds[J]. The Journal of the Acoustical Society of America, 2023, 153(4): 2190. doi: 10.1121/10.0017804.
[5]	HUNTER AKINS F, KUPERMAN W A. Range-coherent matched field processing for low signal-to-noise ratio localization[J]. The Journal of the Acoustical Society of America, 2021, 150(1): 270–280. doi: 10.1121/10.0005586.
[6]	BROWN M G. Time-warping in underwater acoustic waveguides[J]. The Journal of the Acoustical Society of America, 2020, 147(2): 898–910. doi: 10.1121/10.0000693.
[7]	BONNEL J, LE TOUZE G, NICOLAS B, et al. Automatic and passive whale localization in shallow water using gunshots[C]. Oceans 2008, Quebec City, Canada, 2008: 1–6. doi: 10.1109/OCEANS.2008.5151937.
[8]	李晓曼, 张明辉, 张海刚, 等. 一种基于模态匹配的浅海波导中宽带脉冲声源的被动测距方法[J]. 物理学报, 2017, 66(9): 094302. doi: 10.7498/aps.66.094302. LI Xiaoman, ZHANG Minghui, ZHANG Haigang, et al. A passive range method of broadband impulse source based on matched-mode processing[J]. Acta Physica Sinica, 2017, 66(9): 094302. doi: 10.7498/aps.66.094302.
[9]	LI Xiaoman, PIAO Shengchun, ZHANG Minghui, et al. A passive source location method in a shallow water waveguide with a single sensor based on Bayesian theory[J]. Sensors, 2019, 19(6): 1452. doi: 10.3390/s19061452.
[10]	BONNEL J, LIN Y T, ELEFTHERAKIS D, et al. Geoacoustic inversion on the New England Mud Patch using warping and dispersion curves of high-order modes[J]. The Journal of the Acoustical Society of America, 2018, 143(5): EL405–EL411. doi: 10.1121/1.5039769.
[11]	BONNEL J, THODE A, WRIGHT D, et al. Nonlinear time-warping made simple: A step-by-step tutorial on underwater acoustic modal separation with a single hydrophone[J]. The Journal of the Acoustical Society of America, 2020, 147(3): 1897–1926. doi: 10.1121/10.0000937.
[12]	ZHOU Shihong, QI Yubo, and REN Yun. Frequency invariability of acoustic field and passive source range estimation in shallow water[J]. Science China Physics, Mechanics and Astronomy, 2014, 57(2): 225–232. doi: 10.1007/s11433-013-5359-z.
[13]	戚聿波, 周士弘, 张仁和, 等. 一种基于β-warping变换算子的被动声源距离估计方法[J]. 物理学报, 2015, 64(7): 074301. doi: 10.7498/aps.64.074301. QI Yubo, ZHOU Shihong, ZHANG Renhe, et al. A passive source ranging method using the waveguide-invariant-warping operator[J]. Acta Physica Sinica, 2015, 64(7): 074301. doi: 10.7498/aps.64.074301.
[14]	王冬, 郭良浩, 刘建军, 等. 一种基于warping变换的浅海脉冲声源被动测距方法[J]. 物理学报, 2016, 65(10): 104302. doi: 10.7498/aps.65.104302. WANG Dong, GUO Lianghao, LIU Jianjun, et al. Passive impulsive source range estimation based on warping operator in shallow water[J]. Acta Physica Sinica, 2016, 65(10): 104302. doi: 10.7498/aps.65.104302.
[15]	孙凯, 高大治, 高德洋, 等. 多普勒频移和干涉谱联合的水声目标运动参数估计[J]. 声学学报, 2023, 48(1): 50–59. doi: 10.15949/j.cnki.0371-0025.2023.01.001. SUN Kai, GAO Dazhi, GAO Deyang, et al. Estimation of motion parameters of underwater acoustic targets by combining Doppler shift and interference spectrum[J]. Acta Acustica, 2023, 48(1): 50–59. doi: 10.15949/j.cnki.0371-0025.2023.01.001.
[16]	孟瑞洁, 周士弘, 戚聿波. 浅海中运动声源径向速度与距离的无源估计[J]. 声学学报, 2021, 46(6): 983–996. doi: 10.15949/j.cnki.0371-0025.2021.06.019. MENG Ruijie, ZHOU Shihong, and QI Yubo. Passive radial velocity and range estimation of a moving source in shallow water[J]. Acta Acustica, 2021, 46(6): 983–996. doi: 10.15949/j.cnki.0371-0025.2021.06.019.
[17]	WILCOX P D. A rapid signal processing technique to remove the effect of dispersion from guided wave signals[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2003, 50(4): 419–427. doi: 10.1109/TUFFC.2003.1197965.
[18]	GAO Dazhi, WANG Ning, and WANG Haozhong. A dedispersion transform for sound propagation in shallow water waveguide[J]. Journal of Computational Acoustics, 2010, 18(3): 245–257. doi: 10.1142/S0218396X10004188.
[19]	YANG Guangbing, LÜ Liangang, GAO Dazhi, et al. A dedispersion transform method for extracting the normal modes of a shallow water acoustic signal in the Pekeris waveguide[J]. Archives of Acoustics, 2015, 40(1): 11–18. doi: 10.1515/aoa-2015-0002.
[20]	胡春晖, 王好忠, 袁博涵, 等. 消频散阵不变量距离估计方法[J]. 声学学报, 2022, 47(3): 309–320. doi: 10.15949/j.cnki.0371-0025.2022.03.007. HU Chunhui, WANG Haozhong, YUAN Bohan, et al. Source ranging using the dispersionless array invariant[J]. Acta Acustica, 2022, 47(3): 309–320. doi: 10.15949/j.cnki.0371-0025.2022.03.007.
[21]	郭晓乐, 杨坤德, 马远良, 等. 一种基于简正波模态消频散变换的声源距离深度估计方法[J]. 物理学报, 2016, 65(21): 214302. doi: 10.7498/aps.65.214302. GUO Xiaole, YANG Kunde, MA Yuanliang, et al. A source range and depth estimation method based on modal dedispersion transform[J]. Acta Physica Sinica, 2016, 65(21): 214302. doi: 10.7498/aps.65.214302.
[22]	JENSEN F B, KUPERMAN W A, PORTER M B, et al. Computational Ocean Acoustics[M]. 2nd ed. New York: Springer, 2011: 337–341. doi: 10.1007/978-1-4419-8678-8.
[23]	SUN Kai, GAO Dazhi, ZHAO Xiaojing, et al. Estimation of target motion parameters from the tonal signals with a single hydrophone[J]. Sensors, 2023, 23(15): 6881. doi: 10.3390/s23156881.