【摘要】：多目標跟蹤問題是信息融合領域的重點和難點,由于具有很高的軍用和民用價值,歷來受到國內外學者的廣泛關注和研究。隨著基于隨機集理論的多目標跟蹤方法研究的深入,多目標跟蹤領域得到了快速發(fā)展。早期的隨機集跟蹤方法假設新生目標強度是先驗信息,但在真實的復雜場景中,目標新生強度是難以預先獲得的。因此,需要在未知目標新生強度的條件下完成多目標的穩(wěn)定跟蹤。本文研究了隨機集框架下未知目標新生強度的多目標跟蹤問題,主要工作如下:首先,概述了隨機集理論的基本概念以及相關濾波算法,詳細介紹了PHD和CPHD兩種濾波算法,并給出了其在線性高斯條件下的高斯混合實現。其次,介紹了傳統(tǒng)的GM目標新生模型,并針對其不足,詳細研究了自適應目標新生強度的PHD濾波器。對于檢測時雜波和新生目標存在互相制約的問題,介紹了一種目標新生率的估計方法,能夠減小雜波對目標新生檢測的影響。由于在雜波環(huán)境下會出現目標新生時刻的確認滯后現象,不利于后續(xù)的航跡關聯等處理,本文提出了自適應目標新生強度的PHD平滑器,將后向平滑算法與目標新生率估計相結合,經分析及仿真結果驗證,該算法能夠更加準確地估計新生目標的狀態(tài)并獲得新生時刻,可得到更好的跟蹤效果。最后,研究了自適應目標新生強度的CPHD濾波算法,并結合仿真實驗,分析對比了ATBI-CPHD濾波器和ATBI-PHD濾波器的跟蹤性能,結果表明,前者對目標數目的估計更加準確。在未知雜波密度的條件下,提出了自適應目標新生強度CPHD濾波器的改進算法,并給出了其高斯混合實現形式。該濾波器能夠在雜波密度和目標新生強度都未知的條件下完成多目標的穩(wěn)定跟蹤,不僅可以擺脫對新生目標強度作為先驗信息的依賴,并且能夠在線估計場景中的雜波密度。通過仿真實驗,驗證了改進算法的有效性和實用性。
[Abstract]:Multi-target tracking is an important and difficult problem in the field of information fusion. Because of its high military and civilian value, it has always been widely concerned and studied by scholars at home and abroad. With the development of multi-target tracking method based on random set theory, the field of multi-target tracking has been developed rapidly. The early random set tracking method assumes that the intensity of the new target is a priori information, but it is difficult to obtain the intensity of the new target in the real complex scene. Therefore, it is necessary to complete the stable tracking of multiple targets under the condition of unknown target strength. In this paper, we study the multi-target tracking problem of unknown targets in the frame of random set. The main work is as follows: firstly, the basic concepts of random set theory and related filtering algorithms are summarized, and two filtering algorithms, PHD and CPHD, are introduced in detail. Moreover, the mixed realization of Gao Si under the condition of linear Gao Si is given. Secondly, the traditional GM target newborn model is introduced, and the adaptive PHD filter with new strength is studied in detail. This paper introduces a method to estimate the rate of target birth, which can reduce the influence of clutter on the detection of new target. Due to the fact that the confirmation lag of the target birth time will occur in the clutter environment, which is not conducive to the subsequent track correlation processing, a PHD smoother with adaptive target regeneration strength is proposed in this paper. Combining the backward smoothing algorithm with the target birth rate estimation, the analysis and simulation results show that the algorithm can estimate the state of the new target more accurately and obtain the new time, and obtain better tracking effect. Finally, the CPHD filtering algorithm with adaptive target strength is studied, and the tracking performance of ATBI-CPHD filter and ATBI-PHD filter is analyzed and compared with the simulation experiment. The results show that the former is more accurate in estimating the number of targets. Under the condition of unknown clutter density, an improved algorithm of adaptive target freshly intensity CPHD filter is proposed, and its Gao Si hybrid realization form is given. The filter can not only get rid of the dependence on the intensity of the new target as a priori information but also estimate the clutter density in the scene online. The effectiveness and practicability of the improved algorithm are verified by simulation experiments.
【學位授予單位】：西安電子科技大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TN713

【參考文獻】

相關期刊論文 前7條

1 歐陽成;華云;高尚偉;;改進的自適應新生目標強度PHD濾波[J];系統(tǒng)工程與電子技術;2013年12期

2 楊威;付耀文;龍建乾;黎湘;;基于有限集統(tǒng)計學理論的目標跟蹤技術研究綜述[J];電子學報;2012年07期

3 陳白帆;蔡自興;鄒智榮;;一種移動機器人SLAM中的多假設數據關聯方法[J];中南大學學報(自然科學版);2012年02期

4 杜航原;郝燕玲;趙玉新;楊永鵬;;用概率假設密度濾波實現同步定位與地圖創(chuàng)建[J];光學精密工程;2011年12期

5 歐陽成;姬紅兵;張俊根;;一種改進的CPHD多目標跟蹤算法[J];電子與信息學報;2010年09期

6 劉偉峰;文成林;;隨機集多目標跟蹤性能評價指標比較與分析[J];光電工程;2010年09期

7 連峰;韓崇昭;劉偉峰;;未知雜波環(huán)境下的多目標跟蹤算法[J];自動化學報;2009年07期

相關博士學位論文 前2條

1 歐陽成;基于隨機集理論的被動多傳感器多目標跟蹤[D];西安電子科技大學;2012年

2 連峰;基于隨機有限集的多目標跟蹤方法研究[D];西安交通大學;2009年

，

本文編號：2340345