發(fā)布時間：2018-03-14 06:18

  本文選題：超寬帶雷達　切入點：人體目標　出處：《國防科學(xué)技術(shù)大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：短距離人體跟蹤在安全領(lǐng)域應(yīng)用上非常重要,比如機場安檢、穿墻偵察恐怖分子、廢墟中搶救受困人員。超寬帶(Ultra-Wideband,UWB)雷達良好的距離分辨力和穿透能力,使其非常適合短距離人體跟蹤。另一方面,工作環(huán)境的特殊性對超寬帶雷達在目標檢測、鑒別和跟蹤方面提出了更高的要求。為此,本文將超寬帶雷達對人體目標的檢測和跟蹤作為研究的重點。論文首先介紹了超寬帶雷達的系統(tǒng)組成、信號體制及信號處理流程,詳細分析了超寬帶雷達常用的四種信號,并主要介紹了耦合對齊,背景相消,以及多徑效應(yīng)抑制三個問題。其中,耦合對齊能夠提高背景相消抑制耦合強雜波的性能,指數(shù)加權(quán)背景相消則能夠?qū)较蚝头菑较蜻\動的目標都有較好的動目標指示性能,利用時間窗可將多徑和雜波從目標回波中分離出來。動目標指示后進行的目標檢測,主要目的就是判斷目標有無。傳統(tǒng)的恒虛警檢測(Constant False Alarm Rate,CFAR)方法對單個人體目標檢測時,為使目標信息得到很好的保留,往往需要設(shè)置相對較高的虛警概率,但同時背景雜波也會隨之增多;而在對多個相距很近,甚至交叉、重疊時的人體目標進行檢測時,會出現(xiàn)嚴重的目標遮蔽現(xiàn)象。為此,本文提出將通常用于圖像處理的CLEAN算法用于超寬帶雷達人體目標檢測中,相比CFAR算法,CLEAN算法對不同運動狀態(tài)下的單個、多個人體目標均有較好的檢測性能,且能夠有效的抑制雜波、多徑和目標遮蔽、自遮蔽現(xiàn)象,并能夠很好的保留目標的信息,提取出人體多個散射點并記錄下每個散射點的到達時延。本文基于人體多散射點的回波模型,利用CLEAN算法提取出人體多個散射點的量測信息后,結(jié)合最近鄰數(shù)據(jù)關(guān)聯(lián)算法和聯(lián)合概率數(shù)據(jù)關(guān)聯(lián)算法,分別實現(xiàn)了對單個、多個人體目標的距離軌跡跟蹤;并針對聯(lián)合概率數(shù)據(jù)關(guān)聯(lián)算法在回波數(shù)目增多時計算量易出現(xiàn)爆炸現(xiàn)象的問題,提出一種改進的聯(lián)合概率數(shù)據(jù)關(guān)聯(lián)算法。該方法不僅實現(xiàn)了對軌跡交叉的多個人體目標的有效跟蹤,而且通過與聯(lián)合概率數(shù)據(jù)關(guān)聯(lián)算法性能比較,二者的性能相當(dāng),但計算量卻大大減少。
[Abstract]:Short-range human tracking is very important in security applications, such as airport security, detection of terrorists through walls, rescue of trapped people from debris. UWB Ultra-Wideband UWBradar has good range resolution and penetration capability. It is very suitable for short range human body tracking. On the other hand, the particularity of working environment puts forward higher requirements for UWB radar in target detection, identification and tracking. This paper focuses on the detection and tracking of human body target by UWB radar. Firstly, the system composition, signal system and signal processing flow of UWB radar are introduced, and four kinds of signals commonly used in UWB radar are analyzed in detail. Three problems, namely coupling alignment, background cancellation, and multipath effect suppression, are introduced, in which coupling alignment can improve the performance of background cancellation and suppression of coupled strong clutter. Exponential weighted background cancellation can indicate both radial and non-radial moving targets, and multipath and clutter can be separated from target echo by time window. The main purpose is to judge the existence or absence of target. When the traditional constant False Alarm CFAR method is used to detect a single human target, it is necessary to set a relatively high false alarm probability in order to keep the target information well. But at the same time, the background clutter will also increase, and in the detection of a number of close, even cross, overlapping human targets, there will be a serious target masking phenomenon. In this paper, CLEAN algorithm, which is usually used in image processing, is applied to human body target detection of UWB radar. Compared with CFAR algorithm, clear algorithm has better detection performance for multiple human body targets under different moving states. And can effectively suppress clutter, multi-path and target masking, self-masking phenomenon, and can very well retain the information of the target, Multiple human scattering points are extracted and the arrival delay of each scattering point is recorded. Based on the echo model of human body multiple scattering points, the measurement information of human body multiple scattering points is extracted by using CLEAN algorithm. Combined with nearest neighbor data association algorithm and joint probabilistic data association algorithm, the distance trajectory tracking of single or multiple human objects is realized respectively. The joint probabilistic data association algorithm is prone to explosion when the number of echoes increases. An improved joint probabilistic data association algorithm is proposed, which not only realizes the effective tracking of multiple human objects whose tracks are crossed, but also compares the performance of the joint probabilistic data association algorithm with that of the joint probabilistic data association algorithm. But the amount of calculation is greatly reduced.
【學(xué)位授予單位】：國防科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN953
，

本文編號：1610004