基于三特征预测的海杂波中小目标检测方法

TN957.51; 特征检测方法是解决海杂波中小目标检测问题的重要途径,其根据特征值是否在判决区域内判断目标有无,几乎不考虑特征间的时序信息.事实上,历史帧数据与当前帧数据的时序关联性,可以为当前帧特征值的计算提供丰富的先验信息.为此,该文提出了一种使用自回归(AR)模型在特征域对雷达回波进行时序建模和预测的方法,以利用历史帧特征的先验信息.首先,使用AR模型对平均幅度(AA)、相对多普勒峰高(RDPH)、频谱峰均比(FPAR)特征序列进行建模和1步预测分析,验证了对特征序列进行AR建模和预测的可行性.其次,提出利用历史帧特征时序信息作为先验信息的特征值提取方法,在此基础上,提出一种基于三特征...

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Bibliographic Details
Published in雷达学报 Vol. 12; no. 4; pp. 762 - 775
Main Authors 董云龙, 张兆祥, 丁昊, 黄勇, 刘宁波
Format Journal Article
LanguageChinese
Published 海军航空大学 烟台 264001 01.06.2023
Subjects
海杂波
Prior information
先验信息
历史帧特征
Sea clutter
目标检测
Feature prediction
特征预测
Target detection
Historical frame features
Online AccessGet full text
ISSN2095-283X
DOI10.12000/JR23037

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Abstract TN957.51; 特征检测方法是解决海杂波中小目标检测问题的重要途径,其根据特征值是否在判决区域内判断目标有无,几乎不考虑特征间的时序信息.事实上,历史帧数据与当前帧数据的时序关联性,可以为当前帧特征值的计算提供丰富的先验信息.为此,该文提出了一种使用自回归(AR)模型在特征域对雷达回波进行时序建模和预测的方法,以利用历史帧特征的先验信息.首先,使用AR模型对平均幅度(AA)、相对多普勒峰高(RDPH)、频谱峰均比(FPAR)特征序列进行建模和1步预测分析,验证了对特征序列进行AR建模和预测的可行性.其次,提出利用历史帧特征时序信息作为先验信息的特征值提取方法,在此基础上,提出一种基于三特征预测的小目标检测方法,该方法可有效利用AA,RDPH和FPAR的历史帧特征时序信息.最后,使用实测数据验证了所提方法的有效性.
AbstractList TN957.51; 特征检测方法是解决海杂波中小目标检测问题的重要途径,其根据特征值是否在判决区域内判断目标有无,几乎不考虑特征间的时序信息.事实上,历史帧数据与当前帧数据的时序关联性,可以为当前帧特征值的计算提供丰富的先验信息.为此,该文提出了一种使用自回归(AR)模型在特征域对雷达回波进行时序建模和预测的方法,以利用历史帧特征的先验信息.首先,使用AR模型对平均幅度(AA)、相对多普勒峰高(RDPH)、频谱峰均比(FPAR)特征序列进行建模和1步预测分析,验证了对特征序列进行AR建模和预测的可行性.其次,提出利用历史帧特征时序信息作为先验信息的特征值提取方法,在此基础上,提出一种基于三特征预测的小目标检测方法,该方法可有效利用AA,RDPH和FPAR的历史帧特征时序信息.最后,使用实测数据验证了所提方法的有效性.
Author 董云龙
张兆祥
丁昊
黄勇
刘宁波
AuthorAffiliation 海军航空大学 烟台 264001
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ZHANG Zhaoxiang
HUANG Yong
DONG Yunlong
DING Hao
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Keywords 海杂波
Prior information
先验信息
历史帧特征
Sea clutter
目标检测
Feature prediction
特征预测
Target detection
Historical frame features
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Snippet TN957.51; 特征检测方法是解决海杂波中小目标检测问题的重要途径,其根据特征值是否在判决区域内判断目标有无,几乎不考虑特征间的时序信息.事实上,历史帧数据与当前帧数据...
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Title 基于三特征预测的海杂波中小目标检测方法
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