基于紅外搜索跟蹤系統(tǒng)的低慢小目標檢測技術(shù)研究
發(fā)布時間：2018-12-17 18:16
【摘要】：紅外搜索跟蹤IRST系統(tǒng)因為其在雷達探測的低空盲區(qū)依然能夠?qū)Φ吐∧繕诉M行檢測,因此被廣泛的用作雷達的補盲設(shè)備。而低慢小目標往往是具有較大威脅能力的目標,當前對低慢小目標的檢測不斷提出更高的要求,因此IRST的低慢小目標檢測能力是評估其性能的重要指標,本文結(jié)合IRST工作的實際,提出了切實可行的一套目標檢測算法。本文第二章討論了提取天空背景的工作,針對IRST捕獲圖像的復雜性和實際情況,設(shè)計了一種全天空場景、全地面場景以及天空地面混合場景的識別算法,通過這樣的算法可以提取出全天空背景圖片和天空地面混合背景的圖片,又進一步研究了天空地面混合場景中天空背景區(qū)域的提取方法,由于傳統(tǒng)分割算法的局限性,本文設(shè)計了Gabor濾波描述局部場景的方法提取天空地面混合場景中的天空背景區(qū)域。通過對比發(fā)現(xiàn)本文提出的天空區(qū)域提取方法具有可靠性高實用性強的特點。本文第三章討論了單幀目標的檢測算法,本文提出了一種基于多尺度目標模型的目標檢測算法,同時通過實驗對比發(fā)現(xiàn)本文算法在分割門限取的最低的時候引入的虛警點數(shù)量不是太多,同時對天空背景中的干擾面目標和殘留的地面背景具有一定的抑制作用。另外,本文算法檢測出來的目標保持了弱小目標的成像特點,在圖像上呈現(xiàn)孤立點分布,為多幀聯(lián)合檢測提取真實目標算法提供了支持。本文第四章研究了多潛在目標條件下提取真實目標的工作,針對真實目標在一定時間內(nèi)多幀關(guān)聯(lián)圖像中呈現(xiàn)勻速直線運動特點,而噪聲則在多幀圖像中呈現(xiàn)隨機分布的特點,本文提出多步PPU濾波算法和多步LPPU算法對潛在目標點進行監(jiān)測,從中提取出符合勻速直線運動的觀測點作為真實目標,排除雜亂分布的噪聲虛警點,通過實驗發(fā)現(xiàn)該算法的檢測效果比較好,在雜波強度較高的時候依然能夠具有較好的檢測正確率。
[Abstract]:Infrared search and tracking (IRST) system is widely used as a blind equipment for radar because it can detect low slow small targets in the low altitude blind area detected by radar. However, low slow small target is often the target with great threat ability, so the detection ability of low slow small target in IRST is an important index to evaluate its performance. Based on the practice of IRST, a set of feasible target detection algorithm is proposed in this paper. In the second chapter, the work of extracting sky background is discussed. Aiming at the complexity and actual situation of IRST capture image, a recognition algorithm of whole sky scene, all ground scene and sky ground mixed scene is designed. Through this algorithm, we can extract the whole sky background image and the sky ground mixed background image, and further study the sky background region extraction method in the sky ground mixed scene, because of the limitation of the traditional segmentation algorithm. In this paper, Gabor filter is designed to describe the local scene. By comparison, it is found that the sky region extraction method proposed in this paper has the characteristics of high reliability and high practicability. In the third chapter, we discuss the single frame target detection algorithm, and propose a multi-scale target model based target detection algorithm. At the same time, the experimental results show that the number of false alarm points introduced in this algorithm is not too many when the threshold is the lowest, and it can restrain the interference surface target and the residual ground background in the sky background to a certain extent. In addition, the target detected in this paper keeps the imaging characteristics of small and weak target, and presents outlier distribution on the image, which provides support for multi-frame joint detection and extraction of real target algorithm. In the fourth chapter, we study the work of extracting real targets under the condition of multiple potential targets, aiming at the characteristics of the real targets showing uniform linear motion in multiple frames of correlation images in a certain time, while the noise is randomly distributed in multiple frames of images. In this paper, a multi-step PPU filtering algorithm and a multi-step LPPU algorithm are proposed to monitor the potential target points, from which the observation points which accord with the uniform linear motion are extracted as the real targets, and the noise false alarm points of the clutter distribution are eliminated. The experimental results show that the algorithm has a good detection effect and can still have a good detection accuracy when the clutter intensity is high.
【學位授予單位】：國防科學技術(shù)大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP391.41;TN219
[Abstract]:Infrared search and tracking (IRST) system is widely used as a blind equipment for radar because it can detect low slow small targets in the low altitude blind area detected by radar. However, low slow small target is often the target with great threat ability, so the detection ability of low slow small target in IRST is an important index to evaluate its performance. Based on the practice of IRST, a set of feasible target detection algorithm is proposed in this paper. In the second chapter, the work of extracting sky background is discussed. Aiming at the complexity and actual situation of IRST capture image, a recognition algorithm of whole sky scene, all ground scene and sky ground mixed scene is designed. Through this algorithm, we can extract the whole sky background image and the sky ground mixed background image, and further study the sky background region extraction method in the sky ground mixed scene, because of the limitation of the traditional segmentation algorithm. In this paper, Gabor filter is designed to describe the local scene. By comparison, it is found that the sky region extraction method proposed in this paper has the characteristics of high reliability and high practicability. In the third chapter, we discuss the single frame target detection algorithm, and propose a multi-scale target model based target detection algorithm. At the same time, the experimental results show that the number of false alarm points introduced in this algorithm is not too many when the threshold is the lowest, and it can restrain the interference surface target and the residual ground background in the sky background to a certain extent. In addition, the target detected in this paper keeps the imaging characteristics of small and weak target, and presents outlier distribution on the image, which provides support for multi-frame joint detection and extraction of real target algorithm. In the fourth chapter, we study the work of extracting real targets under the condition of multiple potential targets, aiming at the characteristics of the real targets showing uniform linear motion in multiple frames of correlation images in a certain time, while the noise is randomly distributed in multiple frames of images. In this paper, a multi-step PPU filtering algorithm and a multi-step LPPU algorithm are proposed to monitor the potential target points, from which the observation points which accord with the uniform linear motion are extracted as the real targets, and the noise false alarm points of the clutter distribution are eliminated. The experimental results show that the algorithm has a good detection effect and can still have a good detection accuracy when the clutter intensity is high.
【學位授予單位】：國防科學技術(shù)大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP391.41;TN219
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