【摘要】：鑒于多目標(biāo)跟蹤技術(shù)在信息感知領(lǐng)域的重要地位,大量研究者多年來(lái)一直持續(xù)對(duì)多目標(biāo)跟蹤技術(shù)進(jìn)行研究。目前針對(duì)協(xié)作式目標(biāo)的跟蹤技術(shù)已經(jīng)比較成熟,針對(duì)一般非協(xié)作目標(biāo)的跟蹤技術(shù)也正在完善之中,但是針對(duì)典型對(duì)抗性非協(xié)作軍事目標(biāo)的跟蹤技術(shù)仍面臨諸多困難。這些困難或源自目標(biāo)和環(huán)境特性,或源自傳感器本身。本文以典型多目標(biāo)跟蹤系統(tǒng)面臨的復(fù)雜目標(biāo)、環(huán)境和傳感器觀測(cè)條件下多目標(biāo)跟蹤需求為牽引,對(duì)多目標(biāo)跟蹤方法進(jìn)行了系統(tǒng)深入的學(xué)習(xí)、研究和探索,論文主要工作如下:第二章簡(jiǎn)要介紹了傳統(tǒng)多目標(biāo)跟蹤方法、基于隨機(jī)有限集(Random Finite Set,RFS)的多目標(biāo)跟蹤方法和多目標(biāo)跟蹤性能評(píng)估方法的理論基礎(chǔ),為后續(xù)章節(jié)論述做好鋪墊。首先介紹傳統(tǒng)多目標(biāo)跟蹤方法,給出了單目標(biāo)貝葉斯濾波的具體推導(dǎo)過(guò)程,闡述了Kalman濾波算法與單目標(biāo)貝葉斯濾波的關(guān)系,并解釋了傳統(tǒng)多目標(biāo)跟蹤方法如何通過(guò)數(shù)據(jù)關(guān)聯(lián)技術(shù),將多目標(biāo)跟蹤問(wèn)題分解為若干并行單目標(biāo)貝葉斯濾波問(wèn)題。其次介紹有限集統(tǒng)計(jì)學(xué)(Finite Set Statistics,FISST)和多目標(biāo)貝葉斯濾波,并給出多目標(biāo)貝葉斯濾波矩近似的推導(dǎo)方法及迭代邏輯。最后對(duì)多目標(biāo)跟蹤性能評(píng)估的目的和原理進(jìn)行闡述,介紹了傳統(tǒng)類評(píng)估方法和基于綜合度量的評(píng)估方法,并分析了各種評(píng)估方法的優(yōu)缺點(diǎn)。第三章針對(duì)經(jīng)典聯(lián)合概率數(shù)據(jù)關(guān)聯(lián)(Joint Probabilistic Data Association,JPDA)算法在目標(biāo)密集時(shí)存在的航跡合并問(wèn)題,提出了基于狀態(tài)偏差估計(jì)和去除的方法,并研究了使用目標(biāo)屬性信息輔助的方法�；谄罟烙�(jì)和去除方法僅使用目標(biāo)狀態(tài)信息,在構(gòu)建目標(biāo)-目標(biāo)關(guān)聯(lián)假設(shè)的基礎(chǔ)上給出JPDA算法目標(biāo)狀態(tài)估計(jì)偏差的計(jì)算邏輯,進(jìn)而去除偏差得到無(wú)偏JPDA算法;其與現(xiàn)有嘗試解決航跡合并問(wèn)題算法的仿真結(jié)果對(duì)比,表明了該算法的有效性�；趯傩孕畔⑤o助的JPDA算法要求傳感器能夠提供目標(biāo)屬性信息,且僅在密集目標(biāo)間屬性不一致時(shí)才能實(shí)現(xiàn)航跡合并的有效抑制。本章在屬性輔助JPDA算法方面的研究主要側(cè)重于屬性關(guān)聯(lián)度量及門限的設(shè)計(jì)方面,提出了一種基于奈曼-皮爾遜(Neyman Pearson,NP)準(zhǔn)則的屬性關(guān)聯(lián)度量及門限確定方法,用以克服傳統(tǒng)固定門限所存在的關(guān)聯(lián)性能不穩(wěn)定問(wèn)題。該方法確定的門限是航跡屬性后驗(yàn)概率矢量和傳感器目標(biāo)屬性區(qū)分性能的函數(shù),可使漏檢概率達(dá)到或盡可能接近預(yù)設(shè)值,對(duì)屬性輔助數(shù)據(jù)關(guān)聯(lián)中屬性門技術(shù)研究具有相當(dāng)?shù)膮⒖純r(jià)值。第四章針對(duì)經(jīng)典勢(shì)概率假設(shè)密度(Cardinalized Probability Hypothesis Density,CPHD)濾波器不能處理標(biāo)準(zhǔn)多目標(biāo)馬爾科夫模型中的衍生目標(biāo)模型問(wèn)題,基于FISST推導(dǎo)出了考慮衍生目標(biāo)模型的一般CPHD濾波器迭代公式,并與現(xiàn)有嘗試解決該問(wèn)題的若干方法進(jìn)行了對(duì)比和分析,證明了現(xiàn)有方法僅是所提出方法的特例。推導(dǎo)過(guò)程使用了Faàdi bruno’s行列式規(guī)則,并提出了高階Faàdi bruno’s行列式的可行迭代求解方法,使得所提出的一般CPHD濾波器迭代公式能夠方便工程實(shí)現(xiàn)。仿真結(jié)果表明了所提出方法的有效性。第五章提出了一種適用于非線性觀測(cè)條件的二項(xiàng)分裂高斯混合無(wú)跡Kalman概率假設(shè)密度(Binomial Splitting Gaussian Mixture Unscented Kalman Probability Hypothesis Density,BSGM-UKPHD)濾波器,使得高斯混合概率假設(shè)密度(Gaussian Mixture Probability Hypothesis Density,GM-PHD)濾波器的優(yōu)異性能在非線性觀測(cè)條件下依然能夠得到保持。該算法對(duì)預(yù)測(cè)概率假設(shè)密度(Probabilistic Hypothesis Density,PHD)的每一高斯分量的非線性度進(jìn)行計(jì)算和評(píng)估,當(dāng)非線性度大于某一預(yù)設(shè)門限時(shí),對(duì)高斯分量進(jìn)行二項(xiàng)分解,于是得到一族非線性度較小的高斯分量,從而使得非線性觀測(cè)引起的狀態(tài)更新誤差得到有效抑制,也就使得PHD算法優(yōu)異性能在非線性觀測(cè)條件下得到保持。仿真結(jié)果表明,提出方法性能顯著優(yōu)于兩種傳統(tǒng)方法。第六章提出了一種適用于集中式異步異類觀測(cè)條件的真實(shí)更新時(shí)間高斯混合概率假設(shè)密度(Real Update Time GM-PHD,RUT-GM-PHD)算法。首先分析了集中式異步異類觀測(cè)條件下多目標(biāo)跟蹤算法難以實(shí)施的本質(zhì)原因,發(fā)現(xiàn)問(wèn)題關(guān)鍵在于一般的目標(biāo)運(yùn)動(dòng)模型和觀測(cè)模型難以準(zhǔn)確描述異步異類這種復(fù)雜的觀測(cè)條件,進(jìn)而對(duì)PHD高斯分量引入更新時(shí)間標(biāo)記,從而提出RUT-GM-PHD算法。兩個(gè)較為簡(jiǎn)單的異步異類觀測(cè)條件下的仿真結(jié)果表明了所提出方法的優(yōu)良性能。最后,闡述了一般異步異類觀測(cè)條件下實(shí)施RUT-GM-PHD算法需要注意的若干問(wèn)題,并指出了潛在可行解決途徑。
[Abstract]:In view of the importance of multi-target tracking technology in the field of information perception, a large number of researchers have been studying multi-target tracking technology for many years. At present, the tracking technology for cooperative targets is relatively mature, and the tracking technology for general non-cooperative targets is also being improved, but for typical antagonistic non-cooperative targets. Military target tracking technology is still facing many difficulties. These difficulties arise either from the target and environment characteristics or from the sensor itself. In this paper, the multi-target tracking method is studied systematically and thoroughly based on the complex target that typical multi-target tracking system faces, and the multi-target tracking requirements under the environment and sensor observation conditions. The main work of this paper is as follows: Chapter 2 briefly introduces the traditional multi-target tracking methods, the theoretical basis of the multi-target tracking method based on Random Finite Set (RFS) and the multi-target tracking performance evaluation method, paves the way for the following chapters. The derivation process of standard Bayesian filtering is described. The relationship between Kalman filtering algorithm and single-target Bayesian filtering is expounded. The traditional multi-target tracking method is explained how to decompose the multi-target tracking problem into several parallel single-target Bayesian filtering problems by data association technique. Secondly, finite set statistics is introduced. Ics, FISST) and multi-target Bayesian filtering are presented, and the derivation method and iterative logic of multi-target Bayesian filtering moment approximation are given. Finally, the purpose and principle of multi-target tracking performance evaluation are described, the traditional class evaluation method and the evaluation method based on comprehensive measurement are introduced, and the advantages and disadvantages of various evaluation methods are analyzed. Aiming at the track merging problem of classical Joint Probabilistic Data Association (JPDA) algorithm when targets are dense, a method based on state bias estimation and removal is proposed, and a method aided by target attribute information is studied. Based on the hypothesis of target-target association, the calculation logic of target state estimation bias of JPDA algorithm is given, and then unbiased JPDA algorithm is obtained by eliminating the bias. Compared with the simulation results of existing algorithms attempting to solve track merging problem, the effectiveness of the algorithm is demonstrated. In this chapter, the research of attribute-assisted JPDA algorithm mainly focuses on the design of attribute association measures and thresholds, and proposes an attribute association measure based on Neyman Pearson (NP) criterion. Threshold determination method is used to overcome the instability of correlation performance in traditional fixed threshold. The threshold determined by this method is a function of the posterior probability vector of track attributes and the distinguishing performance of sensor target attributes. It can make the probability of missed detection reach or approach the preset value as far as possible. In the fourth chapter, the iterative formula of CPHD filter considering the derivative target model is deduced based on FISST for the problem that the classical potential probability Hypothesis Density (CPHD) filter can not deal with the derivative target model in the standard multi-objective Markov model. Several methods for solving this problem are compared and analyzed, and it is proved that the existing methods are only special cases of the proposed methods. The Fa di bruno's determinant rule is used in the derivation process, and a feasible iterative method for solving the high-order Fa di bruno's determinant is proposed, which makes the iteration formula of the proposed general CPHD filter convenient for engineering. Simulation results show the effectiveness of the proposed method. In Chapter 5, a binomial splitting Gaussian Mixture Unscented Kalman Probability Hypothesis Density (BSGM-UKPHD) filter is proposed to make the Gaussian mixture approximate. The excellent performance of the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter can still be maintained under nonlinear observation conditions. The algorithm calculates and evaluates the nonlinearity of each Gaussian component of the predicted probability hypothesis density (PHD) when the nonlinearity is large. The binomial decomposition of the Gaussian component at a preset threshold results in a family of Gaussian components with less nonlinearity, which effectively suppresses the state update error caused by nonlinear observations, and consequently maintains the excellent performance of the PHD algorithm under nonlinear observation conditions. In Chapter 6, a Real Update Time GM-PHD (RUT-GM-PHD) algorithm is proposed for centralized asynchronous observation. First, the essential reason why the multi-target tracking algorithm is difficult to implement under centralized asynchronous observation is analyzed. The key of the problem is that it is difficult to describe the asynchronous and asynchronous observational conditions accurately in the general target motion model and observation model, and then the RUT-GM-PHD algorithm is proposed by introducing the update time marker to the PHD Gaussian component. After that, some problems needing attention in implementing RUT-GM-PHD algorithm under general asynchronous and asynchronous observation conditions are expounded, and the potential feasible solutions are pointed out.
【學(xué)位授予單位】：國(guó)防科學(xué)技術(shù)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TN713

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