深空背景紅外弱小目標(biāo)檢測(cè)和跟蹤技術(shù)研究
發(fā)布時(shí)間：2018-11-07 12:22
【摘要】：紅外成像技術(shù)是一種被動(dòng)接受目標(biāo)的紅外熱輻射特性從而獲得目標(biāo)圖像的技術(shù),其優(yōu)點(diǎn)為隱蔽性強(qiáng)、全時(shí)段、觀測(cè)范圍大等。隨著紅外技術(shù)的不斷發(fā)展及深入研究,紅外系統(tǒng)已經(jīng)被廣泛應(yīng)用于監(jiān)控、制導(dǎo)、跟蹤和搜索等軍事及民用場(chǎng)景。其中,紅外弱小目標(biāo)檢測(cè)和跟蹤技術(shù)是研究熱點(diǎn)之一。深空背景下的紅外弱小目標(biāo)具有以下三個(gè)特點(diǎn),因此使得對(duì)它的檢測(cè)和跟蹤變得更為困難:?由于成像距離較遠(yuǎn),觀測(cè)到的目標(biāo)為弱小目標(biāo),目標(biāo)在圖像中僅僅占很少的像素?cái)?shù);?在系統(tǒng)噪聲和背景噪聲的雙重干擾下,目標(biāo)信號(hào)一般較弱,并且很容易淹沒(méi)于強(qiáng)起伏的背景環(huán)境中;?由于觀測(cè)到的是紅外弱小目標(biāo),目標(biāo)沒(méi)有固定的形狀,并且缺少紋理信息,大大減少了可提取的目標(biāo)點(diǎn)特征信息。因此,深空背景條件下的目標(biāo)檢測(cè)和跟蹤算法研究是一個(gè)非常有挑戰(zhàn)的課題,對(duì)其進(jìn)行深入研究具有重要的理論意義和實(shí)際應(yīng)用價(jià)值。本文緊密圍繞深空背景下紅外弱小目標(biāo)檢測(cè)和跟蹤技術(shù)進(jìn)行了深入的研究,主要工作和創(chuàng)新點(diǎn)如下:(1)研究了深空紅外圖像預(yù)處理算法。提出了一種基于方差加權(quán)信息熵為復(fù)雜度指標(biāo)的自適應(yīng)Butterworth高通濾波預(yù)處理算法。該算法首先基于方差加權(quán)信息熵指標(biāo)對(duì)深空紅外圖像背景復(fù)雜度進(jìn)行定量衡量,在此基礎(chǔ)上提出了深空紅外圖像背景復(fù)雜度的概念,然后利用衡量后的參數(shù),調(diào)節(jié)Butterworth高通濾波器系數(shù),對(duì)簡(jiǎn)單背景、高噪聲點(diǎn)干擾和目標(biāo)淹沒(méi)環(huán)境下的背景圖像進(jìn)行了自適應(yīng)處理。實(shí)驗(yàn)結(jié)果表明,基于方差加權(quán)信息熵的Butterworth高通濾波算法可以較好地抑制背景噪聲,從而提高圖像信噪比。(2)研究了深空背景紅外圖像弱小目標(biāo)檢測(cè)算法。針對(duì)傳統(tǒng)Otsu算法分割性能不高和實(shí)時(shí)性差的問(wèn)題,提出了一種二維直方圖斜分Otsu快速迭代算法。該算法首先分析了二維直方圖斜分相比于直分算法的優(yōu)勢(shì),然后結(jié)合迭代的思想,針對(duì)深空背景紅外圖像目標(biāo)的特點(diǎn),利用能量累積算法對(duì)其進(jìn)行優(yōu)化。實(shí)驗(yàn)結(jié)果表明,基于能量累積的二維直方圖斜分Otsu快速迭代算法能夠較好地提升目標(biāo)檢測(cè)性能,同時(shí)滿足實(shí)時(shí)性要求。(3)研究了深空背景紅外圖像弱小目標(biāo)跟蹤算法。提出了一種基于多特征融合的優(yōu)化輔助粒子濾波跟蹤算法。該算法針對(duì)深空紅外弱小目標(biāo)特征缺乏且難于提取的問(wèn)題,首先分析了多特征融合的有效性,利用多特征融合的思想,結(jié)合不同種類特征的特點(diǎn)來(lái)增加目標(biāo)的信息量;然后采用針對(duì)非線性非高斯環(huán)境的粒子濾波跟蹤算法,通過(guò)增加輔助變量的方式優(yōu)化權(quán)值更新,并在算法的濾波過(guò)程中嵌入經(jīng)典的Mean-shift算法,結(jié)合多特征融合的優(yōu)點(diǎn),從而優(yōu)化整個(gè)跟蹤過(guò)程。實(shí)驗(yàn)結(jié)果表明,基于多特征融合的優(yōu)化輔助粒子濾波跟蹤算法,具有較好的準(zhǔn)確性和魯棒性。
[Abstract]:Infrared imaging technology is a kind of passive infrared thermal radiation characteristics of the target to obtain the image technology, its advantages are strong concealment, the entire period of time, a wide range of observations, and so on. With the continuous development and in-depth research of infrared technology, infrared systems have been widely used in surveillance, guidance, tracking and search and other military and civilian scenes. Among them, infrared small and weak target detection and tracking technology is one of the research hotspots. Infrared dim targets in deep space background have the following three characteristics, so it is more difficult to detect and track them:? Because the imaging distance is long, the observed target is a weak target, which accounts for only a small number of pixels in the image;? Under the dual interference of system noise and background noise, the target signal is usually weak and easily submerged in the strong undulating background environment. Because the infrared dim target is observed, the target has no fixed shape, and lacks texture information, which greatly reduces the feature information of the target point that can be extracted. Therefore, the research of target detection and tracking algorithm under the condition of deep space background is a very challenging subject, which has important theoretical significance and practical application value. In this paper, the detection and tracking techniques of infrared dim and small targets in deep space background are deeply studied. The main work and innovations are as follows: (1) the pre-processing algorithm of infrared image in deep space is studied. An adaptive Butterworth high pass filter preprocessing algorithm based on variance weighted information entropy is proposed. The algorithm firstly measures the background complexity of deep space infrared images quantitatively based on variance weighted information entropy index, and then puts forward the concept of background complexity of deep space infrared images, and then uses the measured parameters. By adjusting the coefficients of Butterworth high pass filter, the background images in the environment of simple background, high noise point interference and target inundation are processed adaptively. The experimental results show that the Butterworth high-pass filtering algorithm based on variance weighted information entropy can suppress the background noise and improve the signal-to-noise ratio of the image. (2) the small and weak target detection algorithm of infrared image with deep space background is studied. In order to solve the problem of poor segmentation performance and poor real-time performance of traditional Otsu algorithm, a fast Otsu iterative algorithm with two dimensional histogram diagonal division is proposed. This algorithm firstly analyzes the advantages of two-dimensional histogram oblique division compared with the direct partition algorithm, and then optimizes it by energy accumulation algorithm according to the characteristics of the infrared image of deep space background combined with the idea of iteration. The experimental results show that the fast iterative algorithm of 2-D histogram oblique partition Otsu based on energy accumulation can improve the performance of target detection and meet the real-time requirements. (3) A dim target tracking algorithm for infrared images with deep space background is studied. An optimized particle filter tracking algorithm based on multi-feature fusion is proposed. This algorithm aims at the problem that the feature of infrared dim targets in deep space is scarce and difficult to extract. Firstly, the effectiveness of multi-feature fusion is analyzed. The idea of multi-feature fusion is used to increase the information content of the target by combining the characteristics of different kinds of features. Then the particle filter tracking algorithm aiming at nonlinear non-Gao Si environment is adopted to optimize the weight updating by adding auxiliary variables, and the classical Mean-shift algorithm is embedded in the filtering process of the algorithm, which combines the advantages of multi-feature fusion. In order to optimize the entire tracking process. Experimental results show that the optimized particle filter tracking algorithm based on multi-feature fusion has good accuracy and robustness.
【學(xué)位授予單位】：國(guó)防科學(xué)技術(shù)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP391.41;TN219
本文編號(hào)：2316363
[Abstract]:Infrared imaging technology is a kind of passive infrared thermal radiation characteristics of the target to obtain the image technology, its advantages are strong concealment, the entire period of time, a wide range of observations, and so on. With the continuous development and in-depth research of infrared technology, infrared systems have been widely used in surveillance, guidance, tracking and search and other military and civilian scenes. Among them, infrared small and weak target detection and tracking technology is one of the research hotspots. Infrared dim targets in deep space background have the following three characteristics, so it is more difficult to detect and track them:? Because the imaging distance is long, the observed target is a weak target, which accounts for only a small number of pixels in the image;? Under the dual interference of system noise and background noise, the target signal is usually weak and easily submerged in the strong undulating background environment. Because the infrared dim target is observed, the target has no fixed shape, and lacks texture information, which greatly reduces the feature information of the target point that can be extracted. Therefore, the research of target detection and tracking algorithm under the condition of deep space background is a very challenging subject, which has important theoretical significance and practical application value. In this paper, the detection and tracking techniques of infrared dim and small targets in deep space background are deeply studied. The main work and innovations are as follows: (1) the pre-processing algorithm of infrared image in deep space is studied. An adaptive Butterworth high pass filter preprocessing algorithm based on variance weighted information entropy is proposed. The algorithm firstly measures the background complexity of deep space infrared images quantitatively based on variance weighted information entropy index, and then puts forward the concept of background complexity of deep space infrared images, and then uses the measured parameters. By adjusting the coefficients of Butterworth high pass filter, the background images in the environment of simple background, high noise point interference and target inundation are processed adaptively. The experimental results show that the Butterworth high-pass filtering algorithm based on variance weighted information entropy can suppress the background noise and improve the signal-to-noise ratio of the image. (2) the small and weak target detection algorithm of infrared image with deep space background is studied. In order to solve the problem of poor segmentation performance and poor real-time performance of traditional Otsu algorithm, a fast Otsu iterative algorithm with two dimensional histogram diagonal division is proposed. This algorithm firstly analyzes the advantages of two-dimensional histogram oblique division compared with the direct partition algorithm, and then optimizes it by energy accumulation algorithm according to the characteristics of the infrared image of deep space background combined with the idea of iteration. The experimental results show that the fast iterative algorithm of 2-D histogram oblique partition Otsu based on energy accumulation can improve the performance of target detection and meet the real-time requirements. (3) A dim target tracking algorithm for infrared images with deep space background is studied. An optimized particle filter tracking algorithm based on multi-feature fusion is proposed. This algorithm aims at the problem that the feature of infrared dim targets in deep space is scarce and difficult to extract. Firstly, the effectiveness of multi-feature fusion is analyzed. The idea of multi-feature fusion is used to increase the information content of the target by combining the characteristics of different kinds of features. Then the particle filter tracking algorithm aiming at nonlinear non-Gao Si environment is adopted to optimize the weight updating by adding auxiliary variables, and the classical Mean-shift algorithm is embedded in the filtering process of the algorithm, which combines the advantages of multi-feature fusion. In order to optimize the entire tracking process. Experimental results show that the optimized particle filter tracking algorithm based on multi-feature fusion has good accuracy and robustness.
【學(xué)位授予單位】：國(guó)防科學(xué)技術(shù)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP391.41;TN219
【參考文獻(xiàn)】
相關(guān)期刊論文 前9條
1 程建;;基于粒子濾波與層級(jí)形狀描述的紅外目標(biāo)跟蹤[J];系統(tǒng)工程與電子技術(shù);2011年06期
2 孫殿星;王學(xué)偉;周曉東;涂三軍;;海空背景下確定弱小目標(biāo)潛在區(qū)域的方法[J];紅外技術(shù);2009年11期
3 ;Modified unscented particle filter for nonlinear Bayesian tracking[J];Journal of Systems Engineering and Electronics;2008年01期
4 遲健男;張朝暉;王東署;王志良;;反對(duì)稱雙正交小波在紅外圖像小目標(biāo)檢測(cè)中的應(yīng)用[J];宇航學(xué)報(bào);2007年05期
5 徐韶華;李紅;;基于小波提升框架及小波能量的紅外弱目標(biāo)檢測(cè)方法[J];紅外技術(shù);2006年11期
6 郝穎明,朱楓;2維Otsu自適應(yīng)閾值的快速算法[J];中國(guó)圖象圖形學(xué)報(bào);2005年04期
7 李紅艷,吳成柯;一種基于小波與遺傳算法的小目標(biāo)檢測(cè)算法[J];電子學(xué)報(bào);2001年04期
8 王廣君,田金文,柳健;基于局部熵的紅外圖像小目標(biāo)檢測(cè)[J];紅外與激光工程;2000年04期
9 劉健莊;栗文青;;灰度圖象的二維Otsu自動(dòng)閾值分割法[J];自動(dòng)化學(xué)報(bào);1993年01期
相關(guān)博士學(xué)位論文 前2條
1 胡永生;復(fù)雜背景中紅外弱小目標(biāo)探測(cè)方法研究[D];南京理工大學(xué);2008年
2 楊磊;復(fù)雜背景條件下的紅外小目標(biāo)檢測(cè)與跟蹤算法研究[D];上海交通大學(xué);2006年
相關(guān)碩士學(xué)位論文 前3條
1 趙大恒;紅外圖像目標(biāo)特征提取與分類算法研究[D];西安電子科技大學(xué);2010年
2 楊慧軍;基于特征融合的自動(dòng)目標(biāo)識(shí)別技術(shù)研究[D];上海交通大學(xué);2008年
3 張惠娟;基于貝葉斯濾波的先跟蹤后檢測(cè)算法研究[D];西北工業(yè)大學(xué);2006年
，本文編號(hào)：2316363
本文鏈接：http://sikaile.net/kejilunwen/dianzigongchenglunwen/2316363.html
最近更新
教材專著
