摘要
针对海洋噪声对声呐信号处理和声呐性能评估的背景干扰问题,本文分析了南海南部海域夏季和冬季实测深海环境噪声的深度分布、经验拟合和概率密度分布等统计特性;利用参数化模型得到了噪声谱曲线及其与风速、波高、1 kHz谱之间的经验拟合结果;提出了基于Weibull和Burr分布噪声谱级概率密度拟合方法。结果表明:400 Hz以下800 m以浅夏季噪声谱级中值比冬季高约6 dB,低频航船噪声谱级概率密度(PDF)服从Burr分布,高频风关噪声PDF近似服从高斯分布,夏冬两季噪声统计特性差异与中尺度涡旋分布、声信道传播损失和航船噪声源密度密切相关。
To solve the problem of the background interference caused by oceanic noise on sonar signal processing and sonar performance evaluations,in this study,we analyzed statistical characteristics such as the depth dependence,empirical fitting,and probability density function(PDF)of ambient noise in the deep sea of southern South China Sea(SCS)based on experimental observation data obtained in summer and winter.The correlation coefficients between noise levels(NLs)and wind speed or significant wave height(SWH)are provided.Using a parameterized model,we obtained empirical formulas between the NLs and wind speed,SWH or spectrum levels at 1 kHz.We propose a method of noise spectrum level PDF fitting based on Weibull and Burr distributions.The results show that the median NLs in summer is 6 dB greater than that in winter for frequencies less than 400 Hz and depths less than 800m.The PDFs of low-frequency shipping-radiated and high-frequency wind-driven NLs show agreement with Burr and Gaussian distributions,respectively.The statistical differences of the noise spectrum levels have close relationship with eddy distribution,sound channel transmission loss and distribution of ship noise sources in summer and winter.
作者
杨秋龙
杨坤德
马远良
YANG Qiulong;YANG Kunde;MA Yuanliang(School of Marine Science and Technology,Northwestern Polytechnical University,Xi'an 710072,China;Key Laboratory of Ocean Acoustics and Sensing(Northwestern Polytechnical University),Ministry of Industry and Information Technology,Xi'an 710072,China)
出处
《哈尔滨工程大学学报》
EI
CAS
CSCD
北大核心
2020年第10期1419-1428,共10页
Journal of Harbin Engineering University
基金
国家自然科学基金项目(11574251)
中国博士后基金(2019M663822)
中央高校基本科研业务费专项资金(3102019HHZY030011).
关键词
环境噪声
水下
深海
统计特性
季节特征
相关性
经验模型
概率密度
南海南部
ambient noise
underwater
deep sea
statistical characteristics
seasonal characteristics
correlation
empirical model
probability density
southern South China Sea(SCS)