﻿ 移动单载波水声通信中的有效空时处理技术
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 哈尔滨工程大学学报  2019, Vol. 40 Issue (4): 641-648  DOI: 10.11990/jheu.201805021 0

引用本文

ZHANG Youwen, SHI Shaoqi, HUANG Fupeng, et al. Efficient spatial and temporal processing for highly mobile single-carrier underwater acoustic communications[J]. Journal of Harbin Engineering University, 2019, 40(4), 641-648. DOI: 10.11990/jheu.201805021.

文章历史

1. 哈尔滨工程大学 水声技术重点实验室, 黑龙江 哈尔滨 150001;
2. 哈尔滨工程大学 海洋信息获取与安全工信部重点实验室, 黑龙江 哈尔滨 150001;
3. 哈尔滨工程大学 水声工程学院, 黑龙江 哈尔滨 150001

Efficient spatial and temporal processing for highly mobile single-carrier underwater acoustic communications
ZHANG Youwen 1,2,3, SHI Shaoqi 1,2,3, HUANG Fupeng 1,2,3, SUN Dajun 1,2,3
1. Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, China;
2. Key Laboratory of Marine Information Acquisition and Security(Harbin Engineering University), Ministry of Industry and Information Technology, Harbin 150001, China;
3. College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China
Keywords: underwater acoustic communication    spatial pre-combiner    broadband Doppler compensator    adaptive parallel channel estimation    adaptive recursive least-squares algorithm    phase tracking    decision feedback equalizer    minimum mean square error

1 SIMO水声通信系统模型 1.1 发射系统模型

 $b\left( t \right) = \sum\limits_{n = 1}^{{N_s}} {{x_n}g\left( {t - nT} \right)}$ (1)

 $\begin{array}{*{20}{c}} {{y_m}\left( t \right) = \sum\limits_{l = 0}^{L - 1} {{\beta _{m,l}}\left( t \right)b\left( {\left( {1 + {\kappa _l}} \right)t - {\tau _l}} \right){{\rm{e}}^{{\rm{j}}2{\rm{ \mathsf{ π} }}{f_c}\left( {{\kappa _l}t - {\tau _l}} \right)}}} + }\\ {{\eta _m}\left( t \right)} \end{array}$ (2)

1.2 基于自适应锁相环技术的窄带空时接收机

 ${r_j}\left( n \right) = \sum\limits_{m = 1}^M {\mathit{\boldsymbol{c}}_{j,m}^ * {\mathit{\boldsymbol{y}}_m}\left( n \right)} = \mathit{\boldsymbol{c}}_j^\dagger \mathit{\boldsymbol{Y}}\left( n \right),j = 1,2, \cdots ,J$ (3)

j路接收信号又可表示为：

 ${\mathit{\boldsymbol{r}}_j}\left( n \right) = \sum\limits_{i = - {L_c}}^{{L_a}} {{\mathit{\boldsymbol{h}}_j}\left( i \right)d\left( {n - i} \right){{\rm{e}}^{j{\varphi _j}\left( n \right)}} + {\eta _j}\left( n \right)}$ (4)

 $\begin{array}{*{20}{c}} {{\mathit{\boldsymbol{h}}_j}\left( i \right) = }\\ {{{\left[ {{h_j}\left( {iT + {L_a}T} \right) \cdots {h_j}\left( {iT} \right) \cdots {h_j}\left( {iT - \left( {{L_c} - 1} \right)T} \right)} \right]}^{\rm{T}}}} \end{array}$ (5)

 $\mathit{\boldsymbol{r}}_j^b\left( n \right) = \sum\limits_{i = 1}^{{L_a}} {{\mathit{\boldsymbol{h}}_j}\left( i \right)d\left( {n - i} \right)}$ (6)

 $\mathit{\boldsymbol{r}}_j^b\left( n \right) = \downarrow \mathit{\boldsymbol{r}}_j^b\left( {n - 1} \right) + \mathit{\boldsymbol{\hat h}}\left( 1 \right)\hat d\left( {n - 1} \right)$ (7)

 $\mathit{\boldsymbol{r}}_j^f\left( n \right) = {\mathit{\boldsymbol{r}}_j}\left( n \right){{\rm{e}}^{ - {\rm{j}}{\varphi _j}\left( n \right)}} - \mathit{\boldsymbol{r}}_j^b\left( n \right)$ (8)

 $\tilde d\left( n \right) = \sum\limits_{j = 1}^J {\mathit{\boldsymbol{w}}_j^\dagger \mathit{\boldsymbol{r}}_j^f\left( n \right)}$ (9)

 $e\left( n \right) = \hat d\left( n \right) - \tilde d\left( n \right)$ (10)

 $\mathit{\boldsymbol{c}}\left( {n + 1} \right) = \mathit{\boldsymbol{c}}\left( n \right) + {A_1}\left\{ {\mathit{\boldsymbol{s}}\left( n \right),e\left( n \right)} \right\}$ (11)
 $\mathit{\boldsymbol{s}}\left( n \right) = \left[ {\begin{array}{*{20}{c}} {\mathit{\boldsymbol{Y}}\left( n \right)\mathit{\boldsymbol{w}}_1^ * {{\rm{e}}^{ - {\rm{j}}{\varphi _1}\left( n \right)}}}\\ \vdots \\ {\mathit{\boldsymbol{Y}}\left( n \right)\mathit{\boldsymbol{w}}_J^ * {{\rm{e}}^{ - {\rm{j}}{\varphi _J}\left( n \right)}}} \end{array}} \right]$ (12)

 $\mathit{\boldsymbol{W}}\left( {n + 1} \right) = \mathit{\boldsymbol{W}}\left( n \right) + {A_2}\left( {\left[ {\begin{array}{*{20}{c}} {\mathit{\boldsymbol{r}}_1^f\left( n \right)}\\ \vdots \\ {\mathit{\boldsymbol{r}}_J^f\left( n \right)} \end{array}} \right],e\left( n \right)} \right)$ (13)

 $\theta \left( n \right) = {\mathop{\rm Im}\nolimits} \left\{ {{w^\dagger }\left( n \right)z\left( n \right){{\rm{e}}^{ - j{\varphi _n}}}{{\rm{e}}^ * }\left( n \right)} \right\}$ (14)
 ${\varphi _{n + 1}} = {\varphi _n} + {K_{f1}}\theta \left( n \right) + {K_{f2}}\sum\limits_{i = 0}^n {\theta \left( i \right)}$ (15)

 ${{\mathit{\boldsymbol{\hat h}}}_j}\left( n \right) = \lambda {{\mathit{\boldsymbol{\hat h}}}_j}\left( {n - 1} \right) + \left( {1 - \lambda } \right){\mathit{\boldsymbol{r}}_{j,\varphi }}\left( n \right){{\hat d}^ * }\left( n \right)$ (16)
 ${\mathit{\boldsymbol{r}}_{j,\varphi }}\left( n \right) = {\mathit{\boldsymbol{r}}_j}\left( n \right){{\rm{e}}^{ - {\rm{j}}{\varphi _j}\left( n \right)}}$ (17)
1.3 基于自适应宽带多普勒补偿技术的有效空时接收机技术

 ${\varphi _j}\left( n \right) = {\varphi _j}\left( {n - 1} \right) + 2{\rm{ \mathsf{ π} }}\left( {{I_j}\left( n \right) - 1} \right){f_c}T/2$ (18)
 $\begin{array}{*{20}{c}} {{z_j}\left( k \right) = \left( {{I_j}\left( n \right){r_j}\left( i \right) + \cdots + } \right.}\\ {\left. {\left( {{I_j}\left( n \right) - 1} \right){r_j}\left( {i + 1} \right)} \right){{\rm{e}}^{ - {\rm{j}}{\varphi _j}\left( n \right)}}} \end{array}$ (19)

 ${\mathit{\boldsymbol{z}}_j}\left( n \right) = \sum\limits_{i = - {L_c}}^{{L_a}} {{\mathit{\boldsymbol{h}}_j}\left( i \right)d\left( {n - i} \right){{\rm{e}}^{{\rm{j}}{\varphi _j}\left( n \right)}} + {\eta _j}\left( n \right)}$ (20)

 ${{\mathit{\boldsymbol{\hat h}}}_j}\left( {n + 1} \right) = {{\mathit{\boldsymbol{\hat h}}}_j}\left( n \right) + {\rm{RLS}}\left\{ {{\mathit{\boldsymbol{z}}_j}\left( n \right),\mathit{\boldsymbol{\hat d}}\left( n \right)} \right\}$ (21)

 $\mathit{\boldsymbol{z}}_j^b\left( n \right) = \sum\limits_{i = 1}^{{L_a}} {{{\hat h}_j}\left( i \right)\hat d\left( {n - i} \right)}$ (22)
 $\mathit{\boldsymbol{z}}_j^f\left( n \right) = {\mathit{\boldsymbol{z}}_j}\left( n \right) - \mathit{\boldsymbol{z}}_j^b\left( n \right)$ (23)
 $\tilde d\left( n \right) = \sum\limits_{j = 1}^J {\mathit{\boldsymbol{w}}_j^\dagger \mathit{\boldsymbol{z}}_j^f\left( n \right)}$ (24)

 ${\theta _{n - 1}} = \arg \left\{ {\tilde d\left( {n - 1} \right){{\hat d}^ * }\left( {n - 1} \right)} \right\}$ (25)
 $I\left( n \right) = I\left( {n - 1} \right) + {K_1}{\theta _{n - 1}}$ (26)

2 试验数据处理分析

2.1 基于自适应锁相环技术的窄带空时接收机性能分析

2.2 基于自适应宽带多普勒补偿技术的有效空时接收机性能分析

 ${\rm{SN}}{{\rm{R}}_{{\rm{out}}}} = - 10\lg \left( {\frac{1}{{{N_s}}}\sum\limits_{n = 1}^{{N_s}} {{{\left| {e\left( n \right)} \right|}^2}} } \right)$ (27)
 Download: 图 6 输入通道数为3时误符号率 Fig. 6 SER for pre-combiner with three input channels
 Download: 图 7 输入通道数为4时误符号率 Fig. 7 SER for pre-combiner with four input channels
 Download: 图 8 输入通道数为5时误符号率 Fig. 8 SER for pre-combiner with five input channels
 Download: 图 9 3种模式下空间预综合器输入信道数与均衡器输出信噪比 Fig. 9 Number of input channels versus equalizer output SNR versus for the pre-combiner under three modes

3 结论

1) 基于自适应宽带多普勒补偿技术的有效空时接收机技术的输出信噪比在同一空间预综合器输出通道数的条件下，随着空间预综合器输入通道数的增多而变大；在相同空间预综合器输入通道数的条件下，输出信噪比随着综合后输出通道数的增多先增大后趋于稳定。

2) 在系统误符号率方面，基于自适应锁相环技术的窄带空时接收机在处理高速运动平台间的宽带接收信号时，系统误符号率为0.75，系统完全失效；利用本文提出的基于自适应宽带多普勒补偿技术的有效空时接收机技术处理37帧接收数据时，在空间预综合器输入信道数为5的条件下，有24帧数据误符号率为0，有5帧数据误符号率小于1×10-2，表明本文提出的接收机能够满足移动条件下的水声通信的需求。