【摘要】：多目標(biāo)跟蹤問(wèn)題是信息融合領(lǐng)域的重點(diǎn)和難點(diǎn),由于具有很高的軍用和民用價(jià)值,歷來(lái)受到國(guó)內(nèi)外學(xué)者的廣泛關(guān)注和研究。隨著基于隨機(jī)集理論的多目標(biāo)跟蹤方法研究的深入,多目標(biāo)跟蹤領(lǐng)域得到了快速發(fā)展。早期的隨機(jī)集跟蹤方法假設(shè)新生目標(biāo)強(qiáng)度是先驗(yàn)信息,但在真實(shí)的復(fù)雜場(chǎng)景中,目標(biāo)新生強(qiáng)度是難以預(yù)先獲得的。因此,需要在未知目標(biāo)新生強(qiáng)度的條件下完成多目標(biāo)的穩(wěn)定跟蹤。本文研究了隨機(jī)集框架下未知目標(biāo)新生強(qiáng)度的多目標(biāo)跟蹤問(wèn)題,主要工作如下:首先,概述了隨機(jī)集理論的基本概念以及相關(guān)濾波算法,詳細(xì)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(shí)現(xiàn)。其次,介紹了傳統(tǒng)的GM目標(biāo)新生模型,并針對(duì)其不足,詳細(xì)研究了自適應(yīng)目標(biāo)新生強(qiáng)度的PHD濾波器。對(duì)于檢測(cè)時(shí)雜波和新生目標(biāo)存在互相制約的問(wèn)題,介紹了一種目標(biāo)新生率的估計(jì)方法,能夠減小雜波對(duì)目標(biāo)新生檢測(cè)的影響。由于在雜波環(huán)境下會(huì)出現(xiàn)目標(biāo)新生時(shí)刻的確認(rèn)滯后現(xiàn)象,不利于后續(xù)的航跡關(guān)聯(lián)等處理,本文提出了自適應(yīng)目標(biāo)新生強(qiáng)度的PHD平滑器,將后向平滑算法與目標(biāo)新生率估計(jì)相結(jié)合,經(jīng)分析及仿真結(jié)果驗(yàn)證,該算法能夠更加準(zhǔn)確地估計(jì)新生目標(biāo)的狀態(tài)并獲得新生時(shí)刻,可得到更好的跟蹤效果。最后,研究了自適應(yīng)目標(biāo)新生強(qiáng)度的CPHD濾波算法,并結(jié)合仿真實(shí)驗(yàn),分析對(duì)比了ATBI-CPHD濾波器和ATBI-PHD濾波器的跟蹤性能,結(jié)果表明,前者對(duì)目標(biāo)數(shù)目的估計(jì)更加準(zhǔn)確。在未知雜波密度的條件下,提出了自適應(yīng)目標(biāo)新生強(qiáng)度CPHD濾波器的改進(jìn)算法,并給出了其高斯混合實(shí)現(xiàn)形式。該濾波器能夠在雜波密度和目標(biāo)新生強(qiáng)度都未知的條件下完成多目標(biāo)的穩(wěn)定跟蹤,不僅可以擺脫對(duì)新生目標(biāo)強(qiáng)度作為先驗(yàn)信息的依賴,并且能夠在線估計(jì)場(chǎng)景中的雜波密度。通過(guò)仿真實(shí)驗(yàn),驗(yàn)證了改進(jìn)算法的有效性和實(shí)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(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TN713

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本文編號(hào)：2340345