基于高斯似然的精准水声信道估计

doi:10.12305/j.issn.1001-506X.2024.01.38

系统工程与电子技术 ›› 2023, Vol. 46 ›› Issue (1): 334-344.doi: 10.12305/j.issn.1001-506X.2024.01.38

• 通信与网络 • 上一篇

基于高斯似然的精准水声信道估计

杨光¹^,², 乔培玥¹^,*, 梁俊燕¹, 秦正昌¹, 巩小东³^,⁴, 倪秀辉³^,⁴

1. 青岛理工大学信息与控制工程学院青岛市水声通信及探测装备技术创新中心, 山东青岛 266525
2. 南洋理工大学电气与电子工程学院, 新加坡 639798
3. 山东省科学院海洋仪器仪表研究所, 山东青岛 266318
4. 乌克兰国立技术大学(基辅工学院), 乌克兰基辅 03056

收稿日期:2022-10-28 出版日期:2023-12-28 发布日期:2024-01-11
通讯作者: 乔培玥
作者简介:杨光(1981—), 男, 副教授, 博士, 主要研究方向为运动海洋通信、智能海洋通信
乔培玥(1999—), 女, 硕士研究生, 主要研究方向为水声通信
梁俊燕(1999—), 女, 硕士研究生, 主要研究方向为水声通信
秦正昌(1999—), 男, 硕士研究生, 主要研究方向为水声通信
巩小东(1985—), 男, 副研究员, 硕士, 主要研究方向水声通信、水声电子
倪秀辉(1981—), 男, 副研究员, 硕士, 主要研究方向水声信号处理、模拟电路
基金资助:
国家自然科学基金(61771271);山东省自然科学基金面上项目(ZR2020MF010);山东省自然科学基金面上项目(ZR2020MF001);青岛市源头创新计划-青年专项(19-6-2-4-cg);青岛市关键技术攻关及产业化示范类项目“多节点智能海洋装备布设回收系统关键技术研究和应用示范”(22-3-3-hygg-8-hy)

Accurate underwater acoustic channel estimation based on Gaussian likelihood

Guang YANG¹^,², Peiyue QIAO¹^,*, Junyan LIANG¹, Zhengchang QIN¹, Xiaodong GONG³^,⁴, Xiuhui NI³^,⁴

1. Qingdao Technical Innovation Center for Underwater Acoustic Communication and Detection Equipment, School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, China
2. School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore 639798, Singapore
3. Institute of Oceanographic Instrumentation, Shandong Academy of Sciences, Qingdao 266318, China
4. National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv 03056, Ukraine

Received:2022-10-28 Online:2023-12-28 Published:2024-01-11
Contact: Peiyue QIAO

摘要/Abstract

摘要：

针对时变水声信道的多途干扰问题, 提出基于高斯似然(Gaussian likelihood, GL)的精准水声信道估计算法。GL算法将相邻信道短块的高斯概率密度函数相乘, 乘积仍然服从高斯分布, 且方差变小, 从而进一步提高信道估计的准确性; 采用叠加训练(superimposed training, ST)方案, 将训练序列和符号序列线性叠加, 使训练序列持续传输, 实现对信道的实时跟踪。将ST方案、GL算法和Turbo均衡以迭代的方式相结合, 估计出的符号序列作为虚拟训练序列, 进一步提高时变水声信道的估计和跟踪性能。通过多次迭代计算, 实现时变水声信道的精准估计和实时跟踪。最后, 通过计算机仿真以及胶州湾收发节点水平距离500 m和5.5 km的海上运动实装试验, 验证了所提算法的有效性。

关键词: 时变水声信道, 高斯似然, 叠加训练方案, 虚拟训练序列

Abstract:

Aiming at the multi-path interference problem of the time-varying underwater acoustic channel, an accurate underwater acoustic channel estimation algorithm based on Gaussian likelihood (GL) is proposed. The Gauss probability density functions of the multiple segments are multiplied, the product result still follows the Gauss distribution, and the variance becomes smaller, leading to the improvement of channel estimation accuracy. The superimposed training (ST) scheme is used, where the training sequence and the symbol sequence are linearly superimposed, so that the training sequence can be continuously transmitted, thereby the real-time tracking of the time-varying channel is realized. The ST scheme, GL algorithm, and Turbo equalization are jointly performed in an iterative manner, where the estimated symbol sequence is used as a virtual training sequence to further improve the estimation and tracking performance of the channel. Accurate estimation and real-time tracking of the time-varying underwater acoustic channel are realized through multiple iteration calculation. Finally, the effectiveness of the proposed algorithm is verified by simulation and experimental results (the horizontal distance of the moving transceivers is 500 m and 5.5 km) in Jiaozhou Bay.

Key words: time-varying underwater acoustic channel, Gaussian likelihood (GL), superimposed training (ST) scheme, virtual training sequence

中图分类号:

TN929.3

杨光, 乔培玥, 梁俊燕, 秦正昌, 巩小东, 倪秀辉. 基于高斯似然的精准水声信道估计[J]. 系统工程与电子技术, 2023, 46(1): 334-344.

Guang YANG, Peiyue QIAO, Junyan LIANG, Zhengchang QIN, Xiaodong GONG, Xiuhui NI. Accurate underwater acoustic channel estimation based on Gaussian likelihood[J]. Systems Engineering and Electronics, 2023, 46(1): 334-344.

图/表 18

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参考文献 32

1	SONG A , STOJANOVIC M , CHITRE M . Editorial underwater acoustic communications: where we stand and what is next?[J]. IEEE Journal of Oceanic Engineering, 2019, 44 (1): 1- 6. doi: 10.1109/JOE.2018.2883872
2	QARABAQI P , STOJANOVIC M . Statistical characterization and computationally efficient modeling of a class of underwater acoustic communication channels[J]. IEEE Journal of Oceanic Engineering, 2013, 38 (4): 701- 717. doi: 10.1109/JOE.2013.2278787
3	MARTINS N E , JESUS S M . Blind estimation of the ocean acoustic channel by time-frequency processing[J]. IEEE Journal of Oceanic Engineering, 2006, 31 (3): 646- 656. doi: 10.1109/JOE.2005.850915
4	CAI J J, SU W, ZHANG S N, et al. A semi-blind joint channel estimation and equalization single carrier coherent underwater acoustic communication receiver[C]//Proc. of the OCEANS, 2016.
5	CHO Y H , KO H L . Channel estimation based on adaptive denoising for underwater acoustic OFDM systems[J]. IEEE Access, 2020, 8, 157197- 157210. doi: 10.1109/ACCESS.2020.3018474
6	WANG S J , LIU M L , LI D S . Bayesian learning-based clustered-sparse channel estimation for time-varying underwater acoustic OFDM communication[J]. Sensors, 2021, 21 (14): 4889- 4909. doi: 10.3390/s21144889
7	YIN J W , GE W , HAN X , et al. Frequency-domain equalization with interference rejection combining for single carrier multiple-input multiple-output underwater acoustic communications[J]. The Journal of the Acoustical Society of America, 2020, 147, 138- 143. doi: 10.1121/10.0000711
8	WANG B Y , GUAN X P . Channel estimation for underwater acoustic communications based on orthogonal chirp division multiplexing[J]. IEEE Signal Processing Letters, 2021, 28, 1883- 1887. doi: 10.1109/LSP.2021.3111569
9	FENG X , WANG J F , KUAI X Y , et al. Message passing-based impulsive noise mitigation and channel estimation for underwater acoustic OFDM communications[J]. IEEE Trans. on Vehicular Technology, 2022, 71 (1): 611- 625. doi: 10.1109/TVT.2021.3130061
10	BERGER C R , ZHOU S , PREISIG J C , et al. Sparse channel estimation for multicarrier underwater acoustic communication: from subspace methods to compressed sensing[J]. IEEE Trans. on Signal Processing, 2010, 58 (3): 1708- 1721. doi: 10.1109/TSP.2009.2038424
11	TADAYON A , STOJANOVIC M . Iterative sparse channel estimation and spatial correlation learning for multichannel acoustic OFDM systems[J]. IEEE Journal of Oceanic Engineering, 2019, 44 (4): 820- 836. doi: 10.1109/JOE.2019.2932662
12	尹艳玲, 乔钢, 刘凇佐, 等. 基于基追踪去噪的水声正交频分复用稀疏信道估计[J]. 物理学报, 2015, 64 (6): 227- 234.
	YIN Y L , QIAO G , LIU S Z , et al. Sparse channel estimation of underwater acoustic orthogonal frequency division multiplexing based on basis pursuit denoising[J]. Acta Physica Sinica, 2015, 64 (6): 227- 234.
13	QIAO G , GAN S W , LIU S Z , et al. Self-interference channel estimation algorithm based on maximum-likelihood estimator in in-band full-duplex underwater acoustic communication system[J]. IEEE Access, 2018, 6, 62324- 62334.
14	周跃海, 曹秀岭, 陈东升, 等. 长时延扩展水声信道的联合稀疏恢复估计[J]. 通信学报, 2016, 37 (2): 166- 173.
	ZHOU Y H , CAO X L , CHEN D S , et al. Jointing sparse recovery estimation algorithm of underwater acoustic channels with long time delay spread[J]. Journal on Communications, 2016, 37 (2): 166- 173.
15	ZHOU Y H , TONG F , ZAHNG G Q . Distributed compressed sensing estimation of underwater acoustic OFDM channel[J]. Applied Acoustics, 2017, 117, 160- 166.
16	QIN X , QU F , ZHENG Y R . Bayesian iterative channel estimation and turbo equalization for multiple-input multiple-output underwater acoustic communications[J]. IEEE Journal of Ocea-nic Engineering, 2021, 46 (1): 326- 337.
17	秦晔, 鄢社锋, 徐立军, 等. 用于单载波频域均衡水声通信的可分近似稀疏信道估计[J]. 声学学报, 2018, 43 (4): 526- 537.
	QIN Y , YAN S F , XU L J , et al. Sparse channel estimation using separable approximation for underwater acoustic SC-FDE communications[J]. Acta Acustica, 2018, 43 (4): 526- 537.
18	YANG G , GUO Q H , DING H X , et al. Joint message-passing-based bidirectional channel estimation and equalization with superimposed training for underwater acoustic communications[J]. IEEE Journal of Oceanic Engineering, 2021, 46 (4): 1463- 1476.
19	YANG G , LIU T L , DING H X , et al. Joint channel estimation and generalized approximate messaging passing-based equalization for underwater acoustic communications[J]. IEEE Access, 2021, 9, 56757- 56764.
20	YANG G , WANG L , QIAO P Y , et al. Joint multiple turbo equalization for harsh time-varying underwater acoustic channels[J]. IEEE Access, 2021, 9, 82364- 82372.
21	杨光, 丁寒雪, 郭庆华, 等. 基于叠加训练序列和低复杂度频域Turbo均衡的时变水声信道估计和均衡[J]. 电子与信息学报, 2021, 43 (3): 850- 856.
	YANG G , DING H X , GUO Q H , et al. Estimation and equalization of time-varying underwater acoustic channel based on superimposed training and low-complexity turbo equalization in frequency domain[J]. Journal of Electronics & Information Technology, 2021, 43 (3): 850- 856.
22	GUO Q H , PING L , HUANG D F . A low-complexity iterative channel estimation and detection technique for doubly selective channels[J]. IEEE Trans. on Wireless Communications, 2009, 8 (8): 4340- 4349.
23	GUO Q H , HUANG D F . A concise representation for the soft-in soft-out LMMSE detector[J]. IEEE Communications Letters, 2011, 15 (5): 566- 568.
24	GUO Q H , HUANG D F , NORDHOLM S , et al. Iterative frequency domain equalization with generalized approximate message passing[J]. IEEE Signal Processing Letters, 2013, 20 (6): 559- 562.
25	王凯, 吴立新, 张雪冬, 等. 水声通信加权反馈双向Turbo均衡器[J]. 声学学报, 2021, 46 (6): 835- 846.
	WANG K , WU L X , ZHANG X D , et al. Bidirectional turbo equalizer with weighted feedback for underwater acoustic communication[J]. Acta Acustica, 2021, 46 (6): 835- 846.
26	XI J Y , YAN S F , XU L J , et al. Frequency-time domain turbo equalization for underwater acoustic communications[J]. IEEE Journal of Oceanic Engineering, 2020, 45 (2): 665- 679.
27	奚钧壹, 鄢社锋, 徐立军, 等. 水声通信系统中双向turbo均衡算法[J]. 声学学报, 2018, 43 (5): 771- 778.
	XI J Y , YAN S F , XU L J , et al. Bidirectional turbo equalization for underwater acoustic communications[J]. Acta Acustica, 2018, 43 (5): 771- 778.
28	OROZCO-LUGO A G , LARA M M , MCLERNON D C . Channel estimation using implicit training[J]. IEEE Trans. on Signal Processing, 2004, 52 (1): 240- 254.
29	YIN J W , ZHU G J , HAN X , et al. Iterative channel estimation-based soft successive interference cancellation for multiuser underwater acoustic communications[J]. The Journal of the Acoustical Society of America, 2021, 150 (1): 133- 144.
30	HE C B , JING L Y , XI R , et al. Time-frequency domain turbo equalization for single-carrier underwater acoustic communications[J]. IEEE Access, 2019, 7, 73324- 73335.
31	ZHAO S D , YAN S F , XI J Y . Adaptive turbo equalization for differential OFDM systems in underwater acoustic communications[J]. IEEE Trans. on Vehicular Technology, 2020, 69 (11): 13937- 13941.
32	XI J Y , YAN S F , XU L J , et al. Sparsity-aware adaptive turbo equalization for underwater acoustic communications in the Mariana Trench[J]. IEEE Journal of Oceanic Engineering, 2021, 46 (1): 338- 351.

参数	仿真	胶州湾500 m	胶州湾5.5 km
编码效率	1/4	1/8	1/4
功率比r	0.25∶1	0.25∶1	0.25∶1
短块长度/sym	256, 512	256	256
CP/sym	128	16	16
1帧, 1个数据块	60块, 512 bits	16块, 256 bits	16块, 512 bits
调制方式	-	IQ调制	IQ调制
映射, 系统	QPSK, 基带	QPSK, 单载波	QPSK, 单载波
1个数据块的时间长度/s	-	0.27	0.27
1个符号的时间长度/s	-	2.5×10^－4	2.5×10^－4
中心频率, 滤波	-	12 kHz, 带通	12 kHz, 带通
带宽/kHz	4	4	4
采样频率/kHz	-	96	96
传输速率/(bits/s)	-	3 765	3 765
频带利用率/ (bps/Hz)	-	0.94	0.94
通信距离/m	-	500	5 500
换能器深度/m	-	4	4
水听器深度/m	-	5	5
相对速度/(m/s)	-	2	0.5
SINR/dB	9~12	14	14

参数	数值
通信频率	9~14 kHz(实际使用10~14 kHz)
通信距离	6 000 m(高SINR)
工作深度	2 000 m
电气参数	接收功率0.6 W; 传输功率10~60 W, 内置400 Wh充电电池; RS-232接口
力学参数	长790 mm×宽(直径)130 mm; 重量15 kg(含电池)

块编号	α=auto		α=0		α=1
	迭代次数		迭代次数		迭代次数
	0	1	0	1	0	1
1	0	0	0	0	0	0
2	0	0	0	0	0	0
3	0	0	0	0	0	0
4	0	0	0	0	0	0
5	0	0	0	0	0	0
6	0	0	0	0	0	0
7	0	0	0	0	0	0
8	0	0	0	0	0	0
9	0	0	0	0	0	0
10	0	0	3.6%	0	0	0
11	0	0	11.2%	0	0	0
12	0	0	8.8%	0	0	0
13	0	0	14.4%	0	0	0
14	0	0	0.4%	0	0	0
15	0	0	18.0%	0	0	0
16	13.6%	0	30.8%	26.0%	14.0%	0
均值	0.9%	0	5.5%	1.6%	0.9%	0

块编号	α=auto		α=0		α=1
	迭代次数		迭代次数		迭代次数
	0	1	0	1	0	1
1	11.6	0	45.2	41.2	15.0	1.0
2	14.4	0	40.6	38.1	17.6	3.6
3	3.4	0	29.2	28.8	5.9	0
4	6.7	0	42.8	38.7	10.8	0
5	14.4	0	31.2	28.6	14.4	0.6
6	8.1	0	36.5	20.3	5.5	0
7	0.4	0	14.4	1.2	0.4	0
8	0.2	0	20.1	4.1	0	0
9	0	0	24.1	10.5	0.8	0
10	0.8	0	22.1	3.6	0	0
11	0	0	5.1	0	0	0
12	0	0	26.4	9.5	0	0
13	0	0	3.6	0	0.8	0
14	0	0	5.5	0	0	0
15	0	0	18.3	0	0	0
16	0.8	0	27.6	4.3	0.8	0
均值	3.8	0	24.5	14.3	4.5	0.3

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基于高斯似然的精准水声信道估计

Accurate underwater acoustic channel estimation based on Gaussian likelihood

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