摘要
舰船声呐设备利用声波在水下的传播特性,将采集的声音信号转换为电信号,从而确定水下目标的位置信息、速度信息和形态信息等,是舰船上重要的水声学探测装置。在舰船声呐的正常工作中,噪声信号会对声呐的探测和目标定位精度产生不利影响,常见的噪声源包括舰船螺旋桨噪声、舰船机械噪声、声呐设备自噪声等。本文主要研究舰船声呐设备的自噪声频段分布和形成原理,利用神经网络算法对声呐设备自噪声预报技术进行研究,该研究对改善舰船声呐设备的探测水平、提高声呐采集信号的信噪比、降低声呐设备自噪声等有重要意义。
The propagation characteristics of ship sonar equipment using in underwater acoustic, converting the voice signal collection signal, so as to determine the location information of underwater target velocity information and shape information, the ship is important underwater acoustic detection device. In the normal work of ship sonar, noise signal will adversely affect the positioning accuracy of the sonar target detection and the common noise source, including ship propeller noise, the mechanical noise and self noise of sonar equipment. This paper mainly studies the self noise frequency distribution and forming principle of ship sonar equipment, the sonar equipment was studied from the noise prediction technique using neural network algorithm to improve the level of ship detection sonar equipment on the sonar signal acquisition, improve the signal-to-noise ratio, low noise reduction equipment, self has important significance.
出处
《舰船科学技术》
北大核心
2017年第20期129-131,共3页
Ship Science and Technology
关键词
声呐设备
神经网络
自噪声预报
信噪比
sonar equipment
neural network
self noise prediction
signal to noise ratio
