﻿ 基于数据可视化的舰船短波通信优化研究
 舰船科学技术  2020, Vol. 42 Issue (6): 153-157    DOI: 10.3404/j.issn.1672-7649.2020.06.031 PDF

Research on ship HF communication optimization based on data visualization
ZHAO Min-quan
No. 92785 Unit of PLA, Huludao 125208, China
Abstract: Due to many advantages of coverage area, communication cost and so on, high frequency (HF) communications play an important role in maritime communication support, but it is susceptible to various factors of weather and communication periods. The paper collects short wave communication data between public stations and local stations, and analyzes the influence of time and weather characteristics on SNR of short wave signals. Through the methods of data visualization, the characteristics of the receiving signal power of the short wave communication are analyzed in different weather and different communication periods in the transceiver station, and the weather conditions and time periods of the best communication are obtained. The simulation results show that the method improves the data utilization, and has a wide range of the military application foreground.
Key words: data analysis     HF communication     data visualization     optimization
0 引　言

1 短波通信数据工程

 图 1 方案流程图 Fig. 1 Flowchat
1.1 数据规范化

1.2 数据清洗与分组处理

1.3 特征影响与相关性

TSR确定的情况下，信号功率值样本为 $P_{T,S,R}^{(i)}$ ，则信号功率 ${P_{T,S,R}}$ 的均值为：

 $E({P_{T,S,R}}) = \frac{1}{n}\sum\limits_{i = 1}^n {P_{T,S,R}^{(i)}} \text{。}$

 ${\sigma _{T,S,R}} = \sqrt {\frac{1}{{n - 1}}\sum\limits_{i = 1}^n {{{\left( {P_{T,S,R}^{(i)} - E({P_{T,S,R}})} \right)}^2}} } \text{。}$

 $r = \frac{{\text{Cov}({P_{T,S,R}},X)}}{{\sqrt {\text{Var}({P_{T,S,R}})Var(X)} }}\text{，}$
 $\text{s.t.Cov}({P_{T,S,R}},X) = E(({P_{T,S,R}} - E({P_{T,S,R}}))(X - E(X)))\text{，}$
 $\text{Var}({P_{T,S,R}}) = \frac{1}{{n - 1}}\sum\limits_{i = 1}^n {{{(P_{T,S,R}^{\left( i \right)} - E({P_{T,S,R}}))}^2}} \text{，}$
 $\text{Var}(X) = \frac{1}{{n - 1}}\sum\limits_{i = 1}^n {{{({X^{(i)}} - E(X))}^2}} \text{，}$
 $E(X) = \frac{1}{n}\sum\limits_{i = 1}^n {{X^{(i)}}} \text{，}$
 $X = \{ T,S,R\} \text{。}$
1.4 数据可视化处理

 $K(X,E(X)) = \exp (\frac{{ - {{\left\| {X - E(X)} \right\|}^2}}}{{{{\left( {2{\sigma _x}} \right)}^2}}})\text{，}$
 $X = \{ T,S,R\} \text{，}$
 ${\sigma _x} = \sqrt {\frac{1}{{n - 1}}\sum\limits_{i = 1}^n {{{({X^{(i)}} - E(X))}^2}} } \text{。}$
2 实例仿真 2.1 实验环境

2.2 数据整理与量化

2.3 时间特征与天气特征影响

 图 2 特征影响结果（1） Fig. 2 Influence of characteristics（1）

 图 3 特征影响结果（2） Fig. 3 Influence of characteristics（2）
2.4 散布矩阵与相关性矩阵

 图 4 接收信号散布矩阵图 Fig. 4 Scatter matrix of received signal

 图 5 噪声信号散布矩阵图 Fig. 5 Scatter matrix of noise signal

 图 6 接收信号相关性热图 Fig. 6 Correlation heat-map of received signal

 图 7 噪声信号相关性热图 Fig. 7 Correlation heat-map of noise signal
3 结　语

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