基于小波子带模型匹配的同水域下目标探测  

Target detection in the same water area based on wavelet sub-band model matching

作　　者：李鹏[1,2] 叶方跃 李剑乔 陈展鹏 

机构地区：[1]南京信息工程大学江苏省气象探测与信息处理重点实验室,南京210044 [2]南京信息工程大学江苏省气象传感网络技术工程中心,南京210044 [3]南京邮电大学电子科学与工程学院,南京210023

出　　处：《电子测量与仪器学报》2017年第11期1860-1868,共9页Journal of Electronic Measurement and Instrumentation

基　　金：江苏省第11批六大高峰人才项目(2014-XXRJ-006);江苏省重点研发计划社会发展项目(BE201569);国家自然科学基金(41075115);江苏高校优势学科Ⅱ期建设工程项目资助

摘　　要：针对水下声呐目标搜寻过程中环境复杂、成像分别率低、人工识别目标困难等实际问题,提出基于双树双密度复小波子带模型匹配的方法自动判别水下目标有无。首先利用双树双密度复小波对所传回待测声图进行分解得到不同方向子带,接着采用三参数广义Γ函数对不同方向子带系数分布拟合作为特征项,最后利用含有目标声图与背景声图在分布特征上存在差异性,根据计算两者间KL距离进行相似度匹配并通过设定最优门限对声图目标有无自动判别。实验证明,利用所提出方法对含有多目标声呐图像判别,其检测品质因子达97.2%,并在不同噪声水平下其检测品质因子对比其他方式平均高出10%,具有较高的检出率和一定鲁棒性。Aiming at the problems such as complex environment, low separation rate and difficult to identify the targets in the process of underwater sonar target search, a new method based on dual tree double density complex wavelet sub-band model is proposed to identify the underwater target automatically. Firstly, the dual tree double density complex wavelet is used to decompose the acoustic image to get different direction sub-bands. Then,usingthe three parametersgeneralizesF function of different directional sub-band coefficients to fit as feature items. Finally, usingthe difference in distribution charaeteristicsbetween acoustic target image and background sonarimage calculating the KL distance, the similarityis matchedandthe optimal threshold is setto distinguish the existingtarget automatically. The experiments prove that the proposed methodis applied to the identification of sonar images with muhiple targets, the quality factor of deteetinn is 97.2% and the detection quality factor is 10% higher than that of other methods at different noise levels,and has high detection rate and rotmstness.

关 键 词：声呐 水下目标探测 双树双密度复小波 广义Γ模型 KL距离 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]