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大数据异步传输环境下舰船激光网络回波信号增强处理
Enhanced processing of ship laser network echo signal under asynchronous transmission environment of big data
- DOI:
- 作者:
- 邵芬红, 王芳, 李丽芬
SHAO Fen-hong, WANG Fang, LI Li-fen
- 作者单位:
- 燕京理工学院 信息科学与技术学院，河北 廊坊 065201
School of Information Science and Technology, Yanching Institute of Technology, Langfang 065201, China
- 关键词:
- 异步传输环境;激光网络;回波信号增强;信号采集;异常信号清除;信号恢复
asynchronous transmission environment; laser network; echo signal enhancement; signal acquisition; abnormal signal clearing; signal recovery
- 摘要:
- 针对信号异步传输所导致增强效果不理想问题，提出大数据异步传输环境下舰船激光网络回波信号增强处理方法。采用信号累积增强技术将舰船激光网络范围内若干个回波信号积累在一起，形成一个高强度的回波信号，增强回波信号被采集的概率，确定激光网络正常状态下的分形盒维数与实时网络连接分形盒维数的差，若差值大于阈值即可将实时网络的回波信号定义为异常信号清除掉；针对被清除的异常数据，采用贝叶斯因子分析模型恢复缺失的回波信号，并对回波信号进行去噪处理。实验结果显示，该方法异常信号漏检率与误检率均低于2%，能够彻底抑制信号内的噪声分量，较好地进行信号恢复。
Aiming at the problem that the enhancement effect is not ideal due to the asynchronous transmission of signals, this paper proposes an enhancement processing method of ship laser network echo signal under the asynchronous transmission environment of big data. The signal accumulation and enhancement technology is used to accumulate several echo signals within the scope of the ship laser network to form a high intensity echo signal, enhance the probability of the echo signal being collected, and determine the difference between the fractal box dimension of the laser network in the normal state and the fractal box dimension of the real-time network connection. If the difference is greater than the threshold, the echo signal of the real-time network can be defined as an abnormal signal to be cleared; For the cleared abnormal data, Bayesian factor analysis model is used to recover the missing echo signal, and the echo signal is de-noised. The experimental results show that the rate of missed detection and false detection of abnormal signals are less than 2%, and the noise component in the signal can be completely suppressed, and the signal recovery is better.
2023,45(5): 178-181 收稿日期：2022-08-23
DOI：10.3404/j.issn.1672-7649.2023.05.035
分类号：TN958
基金项目：教育部产学合作协同育人项目（202002090057）
作者简介：邵芬红(1985-),女,讲师,研究方向为计算机应用技术
