多目标击水声信号特征关联方法
Correlation Method of Acoustic Characteristics of Multiple Targets during Water Entry
李研赫 1邹男 1张丽敏 1刘冰 2修贤 1吴宗铮 1傅可一1
作者信息
- 1. 哈尔滨工程大学 水声技术全国重点实验室,黑龙江 哈尔滨 150001;哈尔滨工程大学 海洋信息获取与安全工业和信息化部重点实验室,黑龙江 哈尔滨 150001;哈尔滨工程大学 水声工程学院,黑龙江 哈尔滨 150001
- 2. 中国船舶集团有限公司 系统工程研究院,北京 100094
- 折叠
摘要
目标入水产生的击水声信号形式复杂,在海洋尖刺干扰的影响下易造成漏报和虚警,当多目标不等间隔陆续入水,不同观测节点间同一目标的击水声信号难以正确关联.针对上述问题,提出一种多目标击水声信号关联方法.通过经验模态分解将检测到的瞬态信号分解为固有模态函数(Intrinsic Mode Function,IMF),并提取各阶IMF的频谱等特征,利用灰色关联分析,模糊C均值聚类两种算法实现多击水声信号的关联,为后续的分布式定位提供必要条件.仿真结果表明:当特征参数个数为6 时,两种方法的目标关联正确率分别可达98%和87%;当测量节点个数为7 时,两种方法的目标关联正确率分别可达95%和87%.仿真结果证明了本文方法的可行性和有效性.
Abstract
The underwater acoustic signal generated by a target entering the water is complex which can easily cause false alarm and miss due to the impulsive interference in ocean.In addition,it is difficult to correctly correlate the underwater acoustic signals of the same target between different observation nodes when multiple targets enter the water at different intervals.In order to solve the above problems,a multi-target water-entry acoustic signals correlation method is proposed.The detected transient signal is decomposed into the intrinsic mode function(IMF)through empirical mode decomposition,and the characteristics of the frequency spectrum and so on are extracted.The grey relational analysis method and fuzzy C-means clustering algorithm are used to realize the correlation of multi-target water-entry acoustic signals,providing necessary conditions for the subsequent distributed positioning.The simulated results show that the target correlation accuracy of the two methods can reach 98%and 87%,respectively,when the number of target feature parameters is 6,and the target correlation accuracy of the two methods can reach 95%and 87%,respectively,when the number of observation nodes is 7.The simulated results prove the feasibility and effectiveness of the proposed method.
关键词
击水声信号/多目标关联/瞬态信号/特征关联Key words
water-entry acoustic signal/multi-target correlation/transient signal/feature correlation引用本文复制引用
基金项目
国家自然科学基金(62271162)
声呐技术重点实验室项目(2023-JCJQ-LB-32/09)
出版年
2024
NETL
NSTL
万方数据