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: 254
- HTML全文浏览量: 80
- 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

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图 1 信号仿真

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图 2 20 km信号的DDT结果

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图 3 模态频散常数估计

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图 4 PSD-DDT结果

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图 5 海上试验概况

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图 6 引导声源

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图 7 引导声源的PSD-DDT结果

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图 8 测距结果对比

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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

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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

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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

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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

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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

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