基于因子图的舰船声呐信号特征增强方法研究
Research on Feature Enhancement Method of Ship Sonar Signal Based on Factor Graph
刘轶1
作者信息
- 1. 海装驻北京地区军事代表局,北京 100071
- 折叠
摘要
以舰船声呐检测信号作为研究对象,建立基于时间序列的因子图模型.首先,使用因子图模型描述声呐检测到的水声信号变量.其次,融合连续时间序列场景下的声呐信号,采取全局优化策略提升可信度.最后,将传统卡尔曼滤波算法与因子图算法进行仿真对比,验证算法的效果.仿真结果表明,使用因子图算法获得的信号特征增强效果优于卡尔曼滤波算法.
Abstract
This article takes ship sonar detection signals as the research object and establishes a factor graph model based on time series.Firstly,use a factor graph model to describe the underwater acoustic signal variables detected by sonar.Secondly,integrating sonar signals from continuous time series scenarios and adopting a global optimization strategy to enhance credibility.Finally,compare the traditional Kalman filtering algorithm with the factor graph algorithm through simulation to verify the effectiveness of the algorithm.The simulation results show that the signal feature enhancement effect obtained by using the factor graph algorithm is better than that of the Kalman filtering algorithm.
关键词
因子图/声呐/特征增强Key words
factor plot/sonar/feature enhancement引用本文复制引用
出版年
2024
NETL
NSTL
万方数据