【摘要】：為了提高紅外小目標(biāo)探測(cè)能力，本文以地球靜止軌道凝視探測(cè)器對(duì)地探測(cè)為應(yīng)用背景，提出了兩種對(duì)小目標(biāo)進(jìn)行探測(cè)的算法：三維匹配濾波算法和基于投影算法的目標(biāo)檢測(cè)方法。針對(duì)動(dòng)平臺(tái)固有抖動(dòng)性，凝視探測(cè)器拍攝到的序列圖像中目標(biāo)關(guān)聯(lián)性低這一問(wèn)題，提出一種新的抖動(dòng)補(bǔ)償算法。本文的主要研究工作如下： （1）分析了紅外小目標(biāo)圖像特性，給出了算法仿真輸入的理論依據(jù)； （2）根據(jù)紅外云圖的背景特性，采用最小二乘法原理構(gòu)建了基于灰度的背景模型，以用來(lái)對(duì)序列圖像的抖動(dòng)量進(jìn)行估計(jì)，并對(duì)序列圖像配準(zhǔn)； （3）利用最大信噪比原則和柯西—施瓦茨不等式推導(dǎo)了三維匹配濾波器的一般數(shù)學(xué)公式。對(duì)于序列圖像，給出了三維匹配濾波算法的一般流程并且進(jìn)行了算法仿真，有效地驗(yàn)證了三維匹配濾波算法對(duì)于低信噪比小目標(biāo)的探測(cè)具有非常優(yōu)越的性能，但是三維匹配濾波算法對(duì)于速度失配的運(yùn)動(dòng)目標(biāo)將表現(xiàn)的很差，其要求更加密集的速度方向?yàn)V波器，對(duì)現(xiàn)有計(jì)算機(jī)存儲(chǔ)和計(jì)算能力提出了較高要求； （4）針對(duì)經(jīng)投影算法計(jì)算得到的二維圖像，設(shè)計(jì)了一種引入分層投票理論的非關(guān)聯(lián)點(diǎn)移除算法，有效地對(duì)孤立點(diǎn)和弱相關(guān)點(diǎn)去除，保留了目標(biāo)軌跡；通過(guò)引入hash原理設(shè)計(jì)計(jì)算了對(duì)多目標(biāo)軌跡進(jìn)行分類(lèi)的參數(shù)，，有效地將多目標(biāo)軌跡進(jìn)行分類(lèi)，并且給出了仿真； （5）利用本文研究成果，設(shè)計(jì)開(kāi)發(fā)了基于GUI的紅外小目標(biāo)檢測(cè)軟件系統(tǒng)，可以實(shí)現(xiàn)抖動(dòng)序列生成，待配準(zhǔn)幀抖動(dòng)量計(jì)算，三維匹配濾波算法對(duì)小目標(biāo)檢測(cè)和基于投影算法的目標(biāo)檢測(cè)。 本文所研究的基于時(shí)空域?yàn)V波的紅外小目標(biāo)檢測(cè)可以為在軌目標(biāo)檢測(cè)提供理論基礎(chǔ)。
[Abstract]:In order to improve the detection ability of small infrared target, the geostationary gaze probe is used as the application background in this paper. Two algorithms are proposed to detect small targets: 3D matched filter and projection based target detection. In view of the inherent jitter of the moving platform and the low correlation of the target in the sequence images taken by the staring detector, a new jitter compensation algorithm is proposed. The main research work of this paper is as follows: (1) the characteristics of infrared small target image are analyzed, and the theoretical basis of algorithm simulation input is given. (2) according to the background characteristics of infrared cloud image, The background model based on gray level is constructed by using the least square method to estimate the jitter of the sequence image and to register the sequence image. (3) based on the principle of maximum signal-to-noise ratio (SNR) and Cauchy Schwartz inequality, the general mathematical formula of 3D matched filter is derived. For the sequence images, the general flow of 3D matched filtering algorithm is given and the algorithm simulation is carried out, which effectively verifies that the 3D matched filtering algorithm has very superior performance for detecting low SNR small targets. But the 3D matched filtering algorithm will be very poor for the velocity mismatched moving target, which requires more intensive velocity direction filter, and puts forward a higher demand for the existing computer storage and computing power. (4) for the two-dimensional images calculated by the projection algorithm, a non-correlation point removal algorithm based on the hierarchical voting theory is designed, which can effectively remove the isolated points and weak correlation points and keep the target track. By introducing the principle of hash, the parameters of multi-target trajectory classification are designed and calculated, the multi-target trajectory is effectively classified, and the simulation is given. (5) using the research results of this paper, The infrared small target detection software system based on GUI is designed and developed, which can generate the jitter sequence, calculate the jitter amount of the frame to be registered, detect the small target with 3D matched filtering algorithm and detect the target based on projection algorithm. In this paper, the small infrared target detection based on spatio-temporal filtering can provide a theoretical basis for in-orbit target detection.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TP751

【參考文獻(xiàn)】

相關(guān)期刊論文 前8條

1 崔常嵬,林英,陳景春;低信噪比緩動(dòng)點(diǎn)目標(biāo)的序貫檢測(cè)算法的分析和改進(jìn)[J];電子學(xué)報(bào);2001年06期

2 陳穎,劉鐮斧,李在銘;一種微弱點(diǎn)運(yùn)動(dòng)目標(biāo)的快速統(tǒng)計(jì)檢測(cè)算法[J];電子學(xué)報(bào);2001年12期

3 陳曉斯;樊祥;程正東;朱斌;方義強(qiáng);;基于紅外小目標(biāo)圖像庫(kù)的特征識(shí)別探測(cè)研究[J];彈箭與制導(dǎo)學(xué)報(bào);2013年04期

4 楊衛(wèi)平,李吉成,沈振康;分層投票表決目標(biāo)檢測(cè)方法及其性能分析[J];紅外技術(shù);2003年06期

5 李少軍;朱振福;;采用粒子濾波的先跟蹤后檢測(cè)算法[J];紅外與激光工程;2009年02期

6 楊衛(wèi)平，沈振康;紅外圖像序列小目標(biāo)檢測(cè)預(yù)處理技術(shù)[J];紅外與激光工程;1998年01期

7 鐘圣芳,張兵,盧煥章;一種基于動(dòng)態(tài)規(guī)劃的點(diǎn)目標(biāo)軌跡關(guān)聯(lián)算法[J];計(jì)算機(jī)測(cè)量與控制;2004年08期

8 戎懷陽(yáng);張涌;;基于頻域特征提取的紅外小目標(biāo)跟蹤算法[J];激光與紅外;2012年08期

相關(guān)博士學(xué)位論文 前2條

1 魏長(zhǎng)安;紅外小目標(biāo)檢測(cè)與跟蹤算法研究[D];哈爾濱工業(yè)大學(xué);2009年

2 汪大寶;復(fù)雜背景下的紅外弱小目標(biāo)檢測(cè)與跟蹤技術(shù)研究[D];西安電子科技大學(xué);2010年

本文編號(hào)：2147538