基于线显著性增强的历程图目标点迹跟踪方法
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摘要
方位里历程图中目标点迹的识别跟踪是被动声纳系统目标探测的关键技术之一,其识别跟踪的性能直接影响到被动声纳系统对目标探测的能力。针对方位历程图中弱信号目标点迹跟踪的问题,结合图像显著性滤波方法,将方位-时间二维信号转化为图像目标轨迹检测问题,利用轨迹信息的时空累计效应来进行点迹信号增强与自动跟踪。该方法能很好地从低信噪比的方位历程图中将目标点迹提取出来,试验表明该方法能准确地完成水下目标点迹的识别跟踪。
Target trace point's automatic tracking in BTR(Bearing-Time-Recording)is one of the key technology of passive sonar target detection system,the track performance directly affects the passive sonar system's target detection ability. Aiming at the weak signal's target tracking in BTR,combining with image significant filtering method,orientation-time two-dimensional can be converted to the image target trajectory detection,using trajectory information's cumulative effect of space and time to automatic tracking. This method can pick up the target trace point from the low SNR Bearing-Time-Recording,after tests showed that this method can rapidly and accurately complete the identification of underwater target trace point tracking.
Target trace point's automatic tracking in BTR(Bearing-Time-Recording)is one of the key technology of passive sonar target detection system,the track performance directly affects the passive sonar system's target detection ability. Aiming at the weak signal's target tracking in BTR,combining with image significant filtering method,orientation-time two-dimensional can be converted to the image target trajectory detection,using trajectory information's cumulative effect of space and time to automatic tracking. This method can pick up the target trace point from the low SNR Bearing-Time-Recording,after tests showed that this method can rapidly and accurately complete the identification of underwater target trace point tracking.
引文
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[4]Wang Q,Wang L. An improved subband peak energy detection method[C]//Ocean Acoustics. IEEE,2016:1-5.
[5]蒋小勇,杜选民.强干扰背景下的鱼雷辐射噪声信号检测方法[J].声学技术,2010,29(1):18-22.
[6]王新勇.噪声被动测距中的滤波技术应用研究[J].哈尔滨工程大学学报,2005(1):80-83.
[7]李茜.图像的显著性特征提取[D].上海:上海大学,2008.
[8]Seo H J,Milanfar P. Face Verification Using the LARK Representation[J]. IEEE Transactions on Information Forensics&Security,2011,6(4):1275-1286.
[9]Tupin F,Maitre H,Mangin J F,et al. Detection of linear features in SAR images:application to road network extraction[J]. IEEE Transactions on Geoscience&Remote Sensing,1998,36(2):434-453.
[10]杨卫东,左峥嵘,张天序.基于检测识别的实孔径雷达景象匹配定位方法[J].华中科技大学学报(自然科学版),2005,33(2):25-27.
[11]王琳,师雪艳,张少辉,等.基于Radon变换的遥感图像运动模糊参数精确估计[J].计算机应用研究,2015,32(12):3798-3800.
[12]刘喜武,刘洪,李幼铭.高分辨率Radon变换方法及其在地震信号处理中的应用[J].地球物理学进展,2004,1:8-15.