發(fā)布時間：2017-12-28 03:19

  本文關(guān)鍵詞：隨機(jī)有限集多目標(biāo)跟蹤技術(shù)研究　出處：《國防科學(xué)技術(shù)大學(xué)》2016年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 隨機(jī)有限集 概率假設(shè)密度濾波器 序貫蒙特卡羅 多傳感器偏差校準(zhǔn) 聯(lián)合目標(biāo)跟蹤與識別 多群目標(biāo)跟蹤 圖像目標(biāo)跟蹤 后驗克拉美羅下界

【摘要】：多目標(biāo)跟蹤的主要任務(wù)是依據(jù)傳感器獲取的含噪數(shù)據(jù)來聯(lián)合估計多目標(biāo)個數(shù)及其運動狀態(tài)或者運動航跡,性能穩(wěn)定且高效的多目標(biāo)跟蹤算法是多目標(biāo)跟蹤技術(shù)和多目標(biāo)跟蹤系統(tǒng)研究的核心,也是本課題研究的出發(fā)點和追求目標(biāo)。目前,多目標(biāo)跟蹤技術(shù)正朝著能處理目標(biāo)數(shù)目未知可變、檢測不確定、觀測源不確定、數(shù)據(jù)關(guān)聯(lián)不確定等復(fù)雜多目標(biāo)跟蹤問題的方向蓬勃發(fā)展,其中尤以Ronald Mahler提出的基于隨機(jī)有限集(Random Finite Set,RFS)的一類多目標(biāo)跟蹤方法對這些復(fù)雜的場景具有天然的適應(yīng)性,不需要進(jìn)行復(fù)雜的數(shù)據(jù)關(guān)聯(lián)處理即可對目標(biāo)個數(shù)未知且時變的多個目標(biāo)進(jìn)行聯(lián)合檢測與跟蹤是此類跟蹤算法的最大優(yōu)勢�；赗FS的多目標(biāo)跟蹤算法為目標(biāo)監(jiān)視與防御、無人駕駛與機(jī)器人、遙感、計算機(jī)視覺、生物醫(yī)學(xué)、現(xiàn)代通信等領(lǐng)域包含的復(fù)雜多目標(biāo)跟蹤問題提供了新的解決途徑,代表著多目標(biāo)跟蹤技術(shù)發(fā)展的新方向。本課題重點挑選隨機(jī)有限集框架下聯(lián)合多傳感器偏差與多目標(biāo)狀態(tài)估計技術(shù)、隨機(jī)集濾波器的航跡提取技術(shù)、基于隨機(jī)集濾波器的多群目標(biāo)跟蹤技術(shù)、隨機(jī)集框架下圖像多弱目標(biāo)檢測前跟蹤(Track-Before-Detect,TBD)技術(shù)及隨機(jī)集濾波器的后驗克拉美羅下界(Posterior Cramer-Rao Lower Bound,PCRLB)性能評估技術(shù)這五項研究內(nèi)容進(jìn)行深入研究,取得的主要研究成果如下:第二章提出了一種基于分層點過程及多群多目標(biāo)概率假設(shè)密度(Multi-group Multi-target Probability Hypothesis Density,MGMT-PHD)濾波器的聯(lián)合多傳感器偏差與多目標(biāo)狀態(tài)估計算法。該算法將多傳感器偏差集建模為父過程,多目標(biāo)狀態(tài)集則是與多傳感器偏差相關(guān)聯(lián)的子過程,通過分開對待兩個相互交互的點過程,可以避免對高維增廣狀態(tài)聯(lián)合估計產(chǎn)生的巨大計算量。在利用MGMT-PHD濾波器解決多傳感器偏差和多目標(biāo)狀態(tài)的聯(lián)合估計問題時,由于多傳感器偏差的個數(shù)即為傳感器的個數(shù),即父過程的元素個數(shù)已知,且多個傳感器獨立收集觀測,即觀測集分割情況是明確的,提出了MGMT-PHD濾波器的粒子實現(xiàn)形式,實現(xiàn)了非線性條件下的聯(lián)合多傳感器偏差與多目標(biāo)狀態(tài)估計。仿真實驗考慮了一個目標(biāo)出現(xiàn)、目標(biāo)消失、目標(biāo)軌跡交叉事件出現(xiàn)的典型復(fù)雜多傳感器多目標(biāo)場景,驗證了所提算法的有效性。第三章在載機(jī)與誘餌縱向可分辨的情況下,解決了末制導(dǎo)主動雷達(dá)導(dǎo)引頭攔截戰(zhàn)機(jī)對抗背景下對波束內(nèi)載機(jī)與誘餌的聯(lián)合快速檢測、識別與穩(wěn)定跟蹤問題。主要貢獻(xiàn)為:第一,對現(xiàn)有的加標(biāo)簽粒子PHD(Labeled Particle PHD,L-P-PHD)濾波器存在的一些局限進(jìn)行改進(jìn),提出改進(jìn)的L-P-PHD(Improved L-P-PHD,IL-P-PHD)濾波器;第二,結(jié)合現(xiàn)有的多模型技術(shù),提出能同時對多個機(jī)動目標(biāo)進(jìn)行跟蹤與航跡維持處理的多模il-p-phd(multiplemodelil-p-phd,mm-il-p-phd)濾波器;最后,基于mm-il-p-phd濾波器,結(jié)合基于回波幅度特征的干擾存在性檢測方法以及對抗場景的特征信息,建立了縱向距離維可分的載機(jī)與誘餌的聯(lián)合快速檢測、穩(wěn)定跟蹤與識別處理框架。仿真實驗表明,所提方法可以有效地實現(xiàn)對縱向距離維可分的載機(jī)與誘餌的快速檢測、穩(wěn)定跟蹤與識別處理。第四章將多群目標(biāo)建模為分層點過程,提出了一種基于隨機(jī)有限集的新算法,該算法能聯(lián)合估計群目標(biāo)個數(shù)、估計群中心和群內(nèi)組件的運動狀態(tài)、提取群中心航跡。其基本思想及涉及到的主要工作與貢獻(xiàn)為:第一,對不可分目標(biāo)phd(unresolvedtargetphd,ut-phd)濾波器的觀測更新過程進(jìn)行了具體化,給出了ut-phd濾波器觀測更新方程的具體計算方法,對ut-phd濾波器進(jìn)行加標(biāo)簽處理,利用序貫蒙特卡羅技術(shù)實現(xiàn)了ut-phd濾波器,提出加標(biāo)簽的粒子ut-phd(labeledparticleut-phd,l-p-ut-phd)濾波器,l-p-ut-phd濾波器能在估計多群目標(biāo)個數(shù)、多群目標(biāo)中心狀態(tài)的同時獲取多群目標(biāo)中心的運動軌跡,實現(xiàn)了多群目標(biāo)中心的聯(lián)合檢測與跟蹤;第二,基于群中心狀態(tài)估計結(jié)果提出了更為精確的觀測集分割算法,完成觀測集分割,將觀測集分割結(jié)果分配給每個群目標(biāo)對應(yīng)的單群粒子phd(single-groupparticlephd,sg-p-phd)濾波器,完成群內(nèi)組件狀態(tài)跟蹤與個數(shù)估計,將群組件個數(shù)估計結(jié)果反饋至l-p-ut-phd濾波器。仿真實驗表明,所提方法可以有效地檢測群目標(biāo)的出現(xiàn)與消失、估計群中心的運動狀態(tài)、獲取群中心的航跡及估計群內(nèi)組件的運動狀態(tài)與組件個數(shù)。第五章分別研究了影響區(qū)域不重疊和影響區(qū)域重疊的圖像多弱目標(biāo)tbd技術(shù)。針對標(biāo)準(zhǔn)phd-tbd算法存在對新生目標(biāo)發(fā)現(xiàn)延遲較久、對目標(biāo)個數(shù)估計不準(zhǔn)且存在起伏的問題,提出了能解決這些問題的廣義phd-tbd算法及其粒子實現(xiàn)。對于目標(biāo)影響區(qū)域重疊的圖像多弱目標(biāo)tbd,包含的主要貢獻(xiàn)與創(chuàng)新體現(xiàn)在:第一,建立了影響區(qū)域重疊的圖像目標(biāo)的疊加傳感器觀測模型,導(dǎo)出了對應(yīng)的多目標(biāo)觀測似然函數(shù);第二,基于建立的模型,將mahler提出的近似疊加phd(approximationsuperpositionalphd,as-phd)濾波器引入圖像目標(biāo)跟蹤框架,對as-phd濾波器的狀態(tài)空間進(jìn)行加標(biāo)簽處理,提出了加標(biāo)簽as-phd濾波器,利用smc技術(shù),提出了加標(biāo)簽as-phd濾波器的粒子實現(xiàn),解決低信噪比下影響區(qū)域重疊的圖像多弱目標(biāo)跟蹤問題。仿真實驗驗證了所提算法的有效性。第六章對基于隨機(jī)有限集的濾波器處理復(fù)雜多目標(biāo)跟蹤問題時所能達(dá)到的性能下界及其計算實現(xiàn)問題開展研究。主要貢獻(xiàn)為:第一,推導(dǎo)出了隨機(jī)集框架下能適應(yīng)目標(biāo)數(shù)目未知可變、檢測不確定、觀測源不確定、數(shù)據(jù)關(guān)聯(lián)不確定出現(xiàn)的復(fù)雜多目標(biāo)跟蹤問題的多目標(biāo)pcrlb(multi-targetpcrlb,mt-pcrlb),及其遞推計算表達(dá)式,用以獲取多目標(biāo)跟蹤算法處理此類問題的性能下界;第二,基于IL-P-PHD濾波器獲取的多目標(biāo)航跡,提出了一種高精度的獲取多目標(biāo)航跡和觀測集間關(guān)聯(lián)關(guān)系的數(shù)據(jù)關(guān)聯(lián)新方法;第三,基于獲取的多目標(biāo)航跡和數(shù)據(jù)關(guān)聯(lián)新方法,導(dǎo)出了評估典型雷達(dá)多目標(biāo)跟蹤問題性能下界的MT-PCRLB的具體表達(dá)式。此外,該性能下界可以與目前流行的加標(biāo)簽隨機(jī)集濾波器配套使用,基于加標(biāo)簽隨機(jī)集濾波器獲取的多目標(biāo)航跡及觀測集與航跡的關(guān)聯(lián)關(guān)系,可以實現(xiàn)MT-PCRLB的遞推計算。仿真實驗表明,提出的MT-PCRLB確能定量地衡量處理復(fù)雜多目標(biāo)跟蹤問題的多目標(biāo)跟蹤算法所能達(dá)到的性能下界。第七章總結(jié)全文,并指出了下一步可能的研究方向。
[Abstract]:The main task of multi target tracking is based on noisy data from sensor to joint estimation of target number and its state of motion or motion tracking, multiple target tracking algorithm is stable and efficient is the core system of multi target tracking and multiple target tracking technology, is also the starting point of the research and the pursuit of the goal. At present, multiple target tracking technology is moving can deal with unknown target number variable, detection uncertainty, observation source uncertainty, data association uncertainty complex multi-target tracking problem of vigorous development, which is based on the random finite set especially Ronald proposed by Mahler (Random Finite Set, RFS) for a class of multi target tracking the method has the natural adaptability to the complex scenes, without the need for complex data processing can be carried out joint detection and tracking of multiple targets in a number of unknown and time is the biggest advantage of this tracking algorithm. Provides a new way to solve complex problem of multi target tracking multiple targets RFS tracking algorithm for targets containing surveillance and defense, unmanned robot, remote sensing, computer vision, biomedicine, modern communication based on the field, is a new direction for the development of multi target tracking technology. This research mainly focuses on the selection of random finite set under the framework of joint multi sensor multi target state estimation bias and track technology, random set filter extraction technology, random set filter multi target tracking based on image multi weak targets technology, random set framework of tracking before detection (Track-Before-Detect, TBD) and random set filter posterior Clarke (Posterior Cramer-Rao Lower Bound Rao lower bound, PCRLB) performance evaluation technology of the five studies in-depth study, the main results are as follows: chapter second presents a hierarchical point process and multi group multi-objective probability hypothesis density (Multi-group Multi-target Probability Hypothesis Density, MGMT-PHD) combined with multi sensor and multi bias filter an algorithm of target state estimation. The algorithm of multi-sensor deviation modeling process of multiple target state in the father, is associated with multiple sensor bias associated sub process, through the separate two interacting point processes, can avoid the high dimensional augmented state estimation of the large amount of calculation. To solve the problem of multi sensor joint estimation bias and multi target state in the use of MGMT-PHD filter, due to a number of multi sensor error is the number of sensors, the parent process the number of elements known, and a plurality of sensors that collect independent observation, observation set segmentation is clear, put forward the realization form of MGMT-PHD filter the particles for the combined multi sensor deviation under the condition of nonlinear and multi target state estimation. The simulation experiments consider a typical complex multi-sensor and multi-target scene with the appearance of the target, the disappearance of the target and the crossover of the target track. The validity of the algorithm is verified. The third chapter solves the problem of fast detection, recognition and stabilization of the target and the decoys in the terminal guided Active Radar Seeker under the condition of longitudinal resolution. The main contributions are as follows: first, the particle labelling of existing PHD (Labeled Particle PHD, L-P-PHD) some limitations of the existence of the filter is improved, an improved L-P-PHD (Improved L-P-PHD IL-P-PHD) filter; second, combined with the existing technology of multi model, which can simultaneously on multiple maneuvering target tracking and track maintenance of multimode il-p-phd the (multiplemodelil-p-phd, mm-il-p-phd) filter; finally, based on the mm-il-p-phd filter, combined with the characteristics of the existence of interference echo amplitude detection method and the confrontation scene feature information based on the establishment of a joint rapid detection, carrier and decoy stable tracking and recognition processing frame longitudinal distance separable. Simulation results show that the proposed method can effectively realize the rapid detection, carrier aircraft and decoy longitudinal distance separable stable tracking and recognition. In the fourth chapter, multigroup target is modeled as a hierarchical point process. A new algorithm based on stochastic finite set is proposed. It can jointly estimate the number of group targets, estimate the motion state of group centers and components within clusters, and extract group center tracks. The basic idea and relates to the main work and contribution are as follows: first, the target can be divided into PhD (unresolvedtargetphd, ut-phd) filter observation update process in detail, gives the calculation method of ut-phd filter observation update equation, the ut-phd filter with label processing, ut-phd filter is realized by sequential Monte Carlo technology, proposed tagged particle ut-phd (labeledparticleut-phd, l-p-ut-phd) filter, l-p-ut-phd filter can estimate the number of target multi group and multi group target center state by trajectory take multi group target center, implementation of joint detection and tracking of multiple group target center; second, group center state estimation results is presented. A more accurate segmentation algorithm based on the observation set, complete observation set segmentation, the segmentation results will be the observation set, single particle distribution corresponding to each pH group target The D (single-groupparticlephd, sg-p-phd) filter completes the state tracking and number estimation of components in the cluster, and returns the estimation results of group components to l-p-ut-phd filters. The simulation results show that the proposed method can effectively detect the appearance and disappearance of group targets, estimate the motion state of group centers, get the track of group center, and estimate the motion state and component number of components in the cluster. In the fifth chapter, the image multi weak target TBD technology, which affects the region does not overlap and affects the overlap of the region, is studied. The standard phd-tbd algorithm has a long delay in finding the new target, and the problem of the number of target numbers and the fluctuation of the target number.
【學(xué)位授予單位】：國防科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TN713
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本文編號：1344359