本文關鍵詞： 機動目標跟蹤 強跟蹤 非線性濾波 數據關聯(lián)　出處：《電子科技大學》2014年碩士論文　論文類型：學位論文

【摘要】：無論是在軍事領域或民用領域,目標跟蹤理論及數據關聯(lián)算法的研究都有十分深遠的意義。隨著科技的不斷發(fā)展,各種新的技術手段被應用到目標跟蹤技術中來,但是應用環(huán)境也越來越復雜,如何快速提高目標跟蹤算法及數據關聯(lián)算法的性能成為亟待解決的問題。濾波算法與數據關聯(lián)算法是機動目標跟蹤中的核心和難點,本文著重對這兩方面進行了研究。首先,本文對機動目標跟蹤理論的基本原理及組成部分進行了介紹,又分別對機動目標的運動模型和一些非線性濾波器展開了討論,重點介紹了幾種經典的模型及非線性濾波算法,并提出了兩種基于強跟蹤的非線性濾波改進算法。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,將強跟蹤濾波因子引入到算法中,在保證算法原有特點的基礎上改善了魯棒性差的問題,使這兩種算法在面臨強機動的時候具有實時調整能力。最后,分別通過兩種不同的仿真環(huán)境對這兩種改進算法進行了仿真分析,仿真結果進一步驗證了算法的有效性。其次,本文對于多目標數據關聯(lián)的特點及任務進行了簡要描述,針對多目標數據互聯(lián)算法中的一些經典數據關聯(lián)算法(例如最近鄰域法、概率數據互聯(lián)算法、廣義數據關聯(lián)算法、基于交互多模型的概率數據互聯(lián)算法等)進行了詳細研究,文中詳細介紹了各種經典數據關聯(lián)算法基本原理及推導步驟,并對各種算法性能進行了分析比較。最后采用對單目標數據關聯(lián)算法及多目標數據關聯(lián)算法分開仿真的方法,將單目標數據關聯(lián)算法(概率數據互聯(lián)算法、基于交互多模型的概率數據互聯(lián)算法)應用于一個結合勻速與勻加速的目標上。仿真結果表明,當目標的運動不能用單一模型來描述的時候,基于交互多模型的概率數據互聯(lián)算法性能要優(yōu)于概率數據互聯(lián)算法。對于多目標數據關聯(lián)算法采用一個二維平面上交叉運動的兩目標仿真環(huán)境,對廣義概率數據關聯(lián)算法及聯(lián)合概率數據關聯(lián)算法的性能進行了比較,仿真結果表明,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.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN953

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本文編號：1498339