炮瞄雷达水柱回波显示与识别研究
摘要
在海军现役舰船中××型炮瞄雷达装舰数量多,日常应用广,但是在平时的应用与训练过程中,也暴露了不少问题,主要有:(1)雷达的显示效果不清晰,(2)水柱回波无自动识别功能。本文就是针对以上两个问题进行研究,第一,通过设计采集电路将模拟回波信号转变成数据信号,由网络传送到微机,在便携机上通过编写过滤算法与余辉处理程序,实现该雷达回波在普通微机屏幕上的显示,第二,通过编写连通域识别、图像特征识别、能量带识别等识别算法,并通过水柱回波的跟踪算法进一步提高水柱回波的识别正确率。
本文就是针对以上两个问题进行研究,进而提出有效地解决办法。第一,通过设计采集电路将模拟回波信号转变成数字信号,由网络传送到微机,在便携机上通过编写过滤算法与余辉处理程序,实现该雷达回波在普通微机屏幕上的显示。本文从雷达显示原理的分析入手,首先运用了ARM技术、AD变换技术、锁相环技术,将原雷达设备中的回波从模拟信号转变成数字信号并通过网络送给微机。在雷达信号的显示研究中,运用了中值滤波、高斯滤波、图像平滑、余辉模拟等算法,使雷达水柱回波非常美观。第二,使用连通域识别、图像特征识别、能量带识别等技术,结合水柱回波的跟踪算法,实现水柱回波的自动识别,最终实现主炮射击成绩的自动评定。在水柱回波识别算法中,单帧图像回波识别运用了连通域、图像特征、特征匹配、能量带等算法,在多帧图像回波合并算法中还运用了水柱相关、水柱运动预测等跟踪算法,实现对一个每秒80KB数据量的连续水柱回波的位置的提取,通过提取的水柱位置与被射击的目标的坐标运算,实现主炮射击成绩的自动评定。
实验数据表明,本研究是切实可行的,其显示效果比原来的显示效果有了很大的提高,自动识别效果十分理想,基本能达到95%以上,经实际海上测试数据表明,本研究已经达到了实际应用水平。
It is very large to the quantity of XX model gun arming radar equipped on navy ship in active service.So it is often used.Then many problems are discovered in the application and training.There are mainly two problems broadly existing for XX-model gun arming radar:one is the display unclear;the other is no automatic identification for water column.The thesis has studied and solved the two problems mentioned upon.Firstly,the thesis designed a circuit for collecting radar echo,changing it into digital signals,and sending the signals to compute.The display of radar echo signal would be show on notebook PC. Secondly,in order to increase the veracity of the automatic identification of water column echoes,the thesis programmed to identify algorithm with being connected to field identification,image character identification,energy band identification technology.
The thesis has studied and solved the two problems mentioned upon.Firstly, the thesis designed a circuit for collecting radar echo,changing it into digital signals,and sending the signals to compute.The display of radar echo signal would be show on notebook PC.In more detail,ARM technology,A/D transform and PLL technology were used to transform the analog radar signal into digital signals;And the digital signals would be processed by filter algorithm and after-glow programmed.Then in order to make the display perfect,Mid-value Filter,Gauss Filter,Image Smooth were also employed.Secondly,in order to realize the automatic identification of water column echoes and automatically evaluate the shooting of primary guns,the thesis programmed to identify algorithm with being connected to field identification,image character identification,energy band identification technology,and track algorithm for water column echo,the algorithm for identification of water column echoes can be divided to two parts.In the single frames image identification of water column, many track algorithms have been used,for example connected domain identification,image character identification,character matching,and energy band identification technology.Then in the multiple frames image identification of water column,many other track algorithms have been used,such as object correlation、forecast of moving object and so on,to abstract the continuous track of the water column echo,which had a data of 80KB/s.The data of the experiment show that automatic identification of primary guns shooting achievement could be done.
Finally,the value and feasibility of the study were proved by the experiment result.The experiment show the proposed method had a better display effect than ever before proposed,and the effect of automatic identification was perfect,then the veracity was 95%,some times could even be much higher.The value and feasibility of the study was also proved by testing data on the sea,the study in the thesis would be used in practice.
本文就是针对以上两个问题进行研究,进而提出有效地解决办法。第一,通过设计采集电路将模拟回波信号转变成数字信号,由网络传送到微机,在便携机上通过编写过滤算法与余辉处理程序,实现该雷达回波在普通微机屏幕上的显示。本文从雷达显示原理的分析入手,首先运用了ARM技术、AD变换技术、锁相环技术,将原雷达设备中的回波从模拟信号转变成数字信号并通过网络送给微机。在雷达信号的显示研究中,运用了中值滤波、高斯滤波、图像平滑、余辉模拟等算法,使雷达水柱回波非常美观。第二,使用连通域识别、图像特征识别、能量带识别等技术,结合水柱回波的跟踪算法,实现水柱回波的自动识别,最终实现主炮射击成绩的自动评定。在水柱回波识别算法中,单帧图像回波识别运用了连通域、图像特征、特征匹配、能量带等算法,在多帧图像回波合并算法中还运用了水柱相关、水柱运动预测等跟踪算法,实现对一个每秒80KB数据量的连续水柱回波的位置的提取,通过提取的水柱位置与被射击的目标的坐标运算,实现主炮射击成绩的自动评定。
实验数据表明,本研究是切实可行的,其显示效果比原来的显示效果有了很大的提高,自动识别效果十分理想,基本能达到95%以上,经实际海上测试数据表明,本研究已经达到了实际应用水平。
It is very large to the quantity of XX model gun arming radar equipped on navy ship in active service.So it is often used.Then many problems are discovered in the application and training.There are mainly two problems broadly existing for XX-model gun arming radar:one is the display unclear;the other is no automatic identification for water column.The thesis has studied and solved the two problems mentioned upon.Firstly,the thesis designed a circuit for collecting radar echo,changing it into digital signals,and sending the signals to compute.The display of radar echo signal would be show on notebook PC. Secondly,in order to increase the veracity of the automatic identification of water column echoes,the thesis programmed to identify algorithm with being connected to field identification,image character identification,energy band identification technology.
The thesis has studied and solved the two problems mentioned upon.Firstly, the thesis designed a circuit for collecting radar echo,changing it into digital signals,and sending the signals to compute.The display of radar echo signal would be show on notebook PC.In more detail,ARM technology,A/D transform and PLL technology were used to transform the analog radar signal into digital signals;And the digital signals would be processed by filter algorithm and after-glow programmed.Then in order to make the display perfect,Mid-value Filter,Gauss Filter,Image Smooth were also employed.Secondly,in order to realize the automatic identification of water column echoes and automatically evaluate the shooting of primary guns,the thesis programmed to identify algorithm with being connected to field identification,image character identification,energy band identification technology,and track algorithm for water column echo,the algorithm for identification of water column echoes can be divided to two parts.In the single frames image identification of water column, many track algorithms have been used,for example connected domain identification,image character identification,character matching,and energy band identification technology.Then in the multiple frames image identification of water column,many other track algorithms have been used,such as object correlation、forecast of moving object and so on,to abstract the continuous track of the water column echo,which had a data of 80KB/s.The data of the experiment show that automatic identification of primary guns shooting achievement could be done.
Finally,the value and feasibility of the study were proved by the experiment result.The experiment show the proposed method had a better display effect than ever before proposed,and the effect of automatic identification was perfect,then the veracity was 95%,some times could even be much higher.The value and feasibility of the study was also proved by testing data on the sea,the study in the thesis would be used in practice.
引文
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[33]顾海军,赵晓晖,王洪革.基于小波模板匹配的目标跟踪.吉林大学学报.信息科学版,2003,21(4)
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[2]许人灿,姜卫东,陈曾平.基于最大熵谱估计的距离-多谱勒成像.现代雷达,2005:15-16页
[3]许人灿,刘朝军,黄小红,陈曾平.基于超分辨 ISAR 成像的飞机目标识别.国防科技大学学报,2002:21-22页
[4]蒋先刚.基于 Delphi 的数字图像处理工程软件设计.中国水利水电出版社,2006,134页,166页
[5]严柏军,郑链,王克勇.基于不变矩特征匹配的快速目标检测算法.红外技术,2001:25-26页
[6][6]杜春雷.ARM 体系结构与编程(第一版).清华大学出版社,2003:24-26页
[7]胡旺,李志蜀,黄奇.基于双窗口和极值压缩的自适应中值滤波.中国图象图形学报,2007,12(1)
[8]张霞,葛芦生.自适应中值滤波算法的研究与应用.微计算机信息,2007(6)
[9]崔鹏,张立昂,吉利久,高立.两种平滑优化算法在一维情况下的比较研究.北京大学学报,2003,39(5)
[10]吕知辛,黄尊灵.图像平滑的高斯加权平均算法.计算机系统应用,1998(7)
[11]贾永红.计算机图像处理与分析.武汉大学出版社,2001:131-133页
[12]H-H Nagel.“Displacement Vectors Derived from Second-Order Intensity Variations in image Sequence”.Computer Graphics and image Processing,1983(21)
[13]A B Hadiashar D Suter.“Robust Optical Flow Computation”.Computer Vision,1998,29(1)
[14]Roland Mech,Michael Wollbom.“A Noise Robust Method for 2D Shape Estimation of Moving Objects in Video Sequences Considering a Moving Camera”.Signal Processing,1998,66(2)
[15]李波,郑锦,李景颉,周兵.基于累积光流的运动对象检测.高技术通讯,2006,16(11)
[16]韩思奇,王蕾.图像分割的阈值法综述.系统工程与电子技术,2002:41-43页
[17]张伟,王森,王阳生.基于中心点的指纹细节结构匹配算法.自动化学报,2003,29(6)
[18]郭美娜,李波.基于树搜索的一种动态空间调度方法.计算机工程与应用,2007,43(14)
[19]陈建祥,桑农.已知最大定位误差的点特征松弛匹配.宇航学报,2006,27(4)
[20]章毓晋.图像分析.清华大学出版社,2005:225-226页
[21]聂煊,赵荣椿.一种视频序列图像中区域匹配方法.西北工业大学学报,2005,23(3)
[22]Del Bimbo,P Pala,L Tanganeli.“Video Retrieval Based on Dynamics of Color Flows”.International Conference on Pattern Recognition,2000:24-25P
[23]H Greonspan,d al.“A Probabilistic Framework for Spatio-Temporal Video Representation and Indexing”.European Conference on Computer Vision,LNCS 1614,Springer-Verlag,Berlin,Germany,2002:14-15P
[24]D DeMenthon.“Spatio-Temporal Segmentation of Video by Hierarchical Mean Shift Analysis”.Statistical Methods in Video Processing,2002:14-15P
[25]Marques F,Molina C.“Object Tracking for Content based Functionalities”.SPIE Visual Communication Image Processing,1997:19-20P
[26]张吴,黄战华,郁道银等.基于差分图像的运动目标跟踪与分割方法的应用研究.光学技术,2005,20(4)
[27]张桂林,徐捷.频域相关技术在图像匹配中的应用.模式识别与人工智能,1997,10(1)
[28]朱明旱,罗大庸.基于帧间差分背景模型的运动物体检测与跟踪.计算机测量与控制,2006,14(8)
[29]B D Lucas,T Kanade.“An iterative Image Registration Technique with an Application to Stereo Vision”.Processing of the International Joint Conference on Artificial Intelligence,1981:21-22P
[30]陈凤东,洪炳镕.基于动态阈值背景差分算法的目标检测方法.哈尔滨工业大学学报,2005,37(7)
[31]Hotter M,Thoma R.“Image Segmentation Based on Object Oriented Mapping Parameter Estimation”.Signal Processing,1988,15(3)
[32]邵文坤,黄爱民,韦庆.动态场景下的运动目标跟踪方法研究.计算机仿真,2006,23(5)
[33]顾海军,赵晓晖,王洪革.基于小波模板匹配的目标跟踪.吉林大学学报.信息科学版,2003,21(4)
[34]S N Jayaramamurthy,Ramesh Jain.“An Approach to the Segmentation of Textured Dynamic Scenes”.Computer Graphics and Image Processing,1983(21)
[35]Robert Thoma,Matthias Bierling.“Motion Compensating Interpolation Considering Covered and Uncovered Background”.Signal Processing:Image Communication,1989(1)
[36]Til Aach,Andre Kan,et al.“Statistical Model-based Change Detection in Moving Video”.Signal Processing,1993,31(2)
[37]A Div G.“Determining Three-Dimensional Motion and Structure form Optical How Generated by Several Moving Objects”.IEEE Trans.on Pattern Analysis Machine Intelligence,1985,7(4)
[38]Wang J,Adelson E.“Representing Moving Images with Layers”.IEEE Trans.on Image Processing,1994,3(5)
[39]王东升,李在铭.空域视频场景监视中运动对象的实时检测与跟踪技术. 信号处理,2005,21(2)
[40]Xu C,Prince P L.“Snakes,Shapes and Gradient Vector Flow”.IEEE,Trans on Image Processing,1998(7)
[41]郭美娜 李波.基于树搜索的一种动态空间调度方法.计算机工程与应用,2007,43(14)
[42]Musmann H G,Hotter M,Ostermann J.“Object-Oriented Analysis Synthesis Coding of Moving Image”.Signal Processing:Image Communication,1989,1(2)
[43]Diehl N.“Object-Oriented Motion Estimation and Segmentation in Image Sequences”.Signal Processing:Image Communication,1991,3(1)
[44]Murray D W,Buxton B F.“Scene Segmentation form Visual Motion Using Global Optimization”.IEEE Trans.on Pattern Analysis and Machine Intelligence,1987,9(2)
[45]Michael M Chang,A Murat Tekalp,M Ibrahim Sezan.“An Algorithm for Simultaneous Motion Estimation and Segmentation”.IEEE Internal Conference on Acoustics.Speech and Signal Processing,Adelaide,Australia,1994:17-19P
[46]张根耀,赵国际,赵宗涛等.运动目标识别中的噪声滤除技术.西北大学学报,2003,33(2)
[47]A Neri,S Colonnese,et al.“Automatic Moving Object and Background Separation”.Signal Processing,1998(66)
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