本文關(guān)鍵詞： 機(jī)動(dòng)目標(biāo)跟蹤 強(qiáng)跟蹤 非線性濾波 數(shù)據(jù)關(guān)聯(lián)　出處：《電子科技大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：無論是在軍事領(lǐng)域或民用領(lǐng)域,目標(biāo)跟蹤理論及數(shù)據(jù)關(guān)聯(lián)算法的研究都有十分深遠(yuǎn)的意義。隨著科技的不斷發(fā)展,各種新的技術(shù)手段被應(yīng)用到目標(biāo)跟蹤技術(shù)中來,但是應(yīng)用環(huán)境也越來越復(fù)雜,如何快速提高目標(biāo)跟蹤算法及數(shù)據(jù)關(guān)聯(lián)算法的性能成為亟待解決的問題。濾波算法與數(shù)據(jù)關(guān)聯(lián)算法是機(jī)動(dòng)目標(biāo)跟蹤中的核心和難點(diǎn),本文著重對這兩方面進(jìn)行了研究。首先,本文對機(jī)動(dòng)目標(biāo)跟蹤理論的基本原理及組成部分進(jìn)行了介紹,又分別對機(jī)動(dòng)目標(biāo)的運(yùn)動(dòng)模型和一些非線性濾波器展開了討論,重點(diǎn)介紹了幾種經(jīng)典的模型及非線性濾波算法,并提出了兩種基于強(qiáng)跟蹤的非線性濾波改進(jìn)算法。這兩種算法分別針對容積卡爾曼濾波器與平方根不敏卡爾曼濾波器,將強(qiáng)跟蹤濾波因子引入到算法中,在保證算法原有特點(diǎn)的基礎(chǔ)上改善了魯棒性差的問題,使這兩種算法在面臨強(qiáng)機(jī)動(dòng)的時(shí)候具有實(shí)時(shí)調(diào)整能力。最后,分別通過兩種不同的仿真環(huán)境對這兩種改進(jìn)算法進(jìn)行了仿真分析,仿真結(jié)果進(jìn)一步驗(yàn)證了算法的有效性。其次,本文對于多目標(biāo)數(shù)據(jù)關(guān)聯(lián)的特點(diǎn)及任務(wù)進(jìn)行了簡要描述,針對多目標(biāo)數(shù)據(jù)互聯(lián)算法中的一些經(jīng)典數(shù)據(jù)關(guān)聯(lián)算法(例如最近鄰域法、概率數(shù)據(jù)互聯(lián)算法、廣義數(shù)據(jù)關(guān)聯(lián)算法、基于交互多模型的概率數(shù)據(jù)互聯(lián)算法等)進(jìn)行了詳細(xì)研究,文中詳細(xì)介紹了各種經(jīng)典數(shù)據(jù)關(guān)聯(lián)算法基本原理及推導(dǎo)步驟,并對各種算法性能進(jìn)行了分析比較。最后采用對單目標(biāo)數(shù)據(jù)關(guān)聯(lián)算法及多目標(biāo)數(shù)據(jù)關(guān)聯(lián)算法分開仿真的方法,將單目標(biāo)數(shù)據(jù)關(guān)聯(lián)算法(概率數(shù)據(jù)互聯(lián)算法、基于交互多模型的概率數(shù)據(jù)互聯(lián)算法)應(yīng)用于一個(gè)結(jié)合勻速與勻加速的目標(biāo)上。仿真結(jié)果表明,當(dāng)目標(biāo)的運(yùn)動(dòng)不能用單一模型來描述的時(shí)候,基于交互多模型的概率數(shù)據(jù)互聯(lián)算法性能要優(yōu)于概率數(shù)據(jù)互聯(lián)算法。對于多目標(biāo)數(shù)據(jù)關(guān)聯(lián)算法采用一個(gè)二維平面上交叉運(yùn)動(dòng)的兩目標(biāo)仿真環(huán)境,對廣義概率數(shù)據(jù)關(guān)聯(lián)算法及聯(lián)合概率數(shù)據(jù)關(guān)聯(lián)算法的性能進(jìn)行了比較,仿真結(jié)果表明,GPDA算法的跟蹤誤差低于JPDA。
[Abstract]:The research of target tracking theory and data association algorithm is of great significance both in military and civilian fields. With the development of science and technology, various new techniques have been applied to target tracking technology. However, the application environment is becoming more and more complex, so how to improve the performance of target tracking algorithm and data association algorithm becomes an urgent problem to be solved. Filtering algorithm and data association algorithm are the core and difficulty of maneuvering target tracking. Firstly, the basic principle and components of maneuvering target tracking theory are introduced, and the motion model and some nonlinear filters of maneuvering target are discussed respectively. In this paper, several classical models and nonlinear filtering algorithms are introduced, and two improved nonlinear filtering algorithms based on strong tracking are proposed. The two algorithms are for volumetric Kalman filter and square root insensitive Kalman filter, respectively. The strong tracking filter factor is introduced into the algorithm, and the problem of poor robustness is improved on the basis of guaranteeing the original characteristics of the algorithm, which makes the two algorithms have the ability to adjust in real time when facing strong maneuverability. The two improved algorithms are simulated and analyzed by two different simulation environments. The simulation results further verify the effectiveness of the algorithm. Secondly, the characteristics and tasks of multi-objective data association are briefly described in this paper. In this paper, some classical data association algorithms (such as nearest neighborhood method, probabilistic data association algorithm, generalized data association algorithm, probabilistic data association algorithm based on interactive multi-model) are studied in detail. In this paper, the basic principle and derivation steps of various classical data association algorithms are introduced in detail, and the performance of these algorithms is analyzed and compared. Finally, the method of separate simulation of single object data association algorithm and multi-objective data association algorithm is adopted. The single objective data association algorithm (probabilistic data association algorithm, probabilistic data association algorithm based on interactive multi-model) is applied to a target with uniform velocity and uniform acceleration. The simulation results show that, When the movement of the target cannot be described in a single model, The performance of probabilistic data association algorithm based on interactive multi-model is better than that of probabilistic data association algorithm. The performance of generalized probabilistic data association algorithm and joint probabilistic data association algorithm are compared. The simulation results show that the tracking error of GPDA is lower than that of JPDA.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN953

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相關(guān)期刊論文 前1條

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本文編號：1498339