本文選題：UAV + 目標(biāo)狀態(tài)估計��； 參考：《北京理工大學(xué)》2015年碩士論文

【摘要】：隨著無人機(jī)應(yīng)用范圍的拓寬和技術(shù)的發(fā)展，無人機(jī)（Unmanned Aerial Vehicle, UAV）協(xié)同目標(biāo)跟蹤問題作為其他任務(wù)過程的子任務(wù)，得到了諸多專家學(xué)者的關(guān)注和發(fā)展。本文針對復(fù)雜環(huán)境下的多UAV協(xié)同地面機(jī)動目標(biāo)跟蹤問題，重點(diǎn)圍繞UAV對地面機(jī)動目標(biāo)的狀態(tài)融合估計跟蹤和UAV觀測航跡規(guī)劃兩個問題進(jìn)行了研究。 首先，針對目標(biāo)跟蹤問題，分析了復(fù)雜環(huán)境下多UAV協(xié)同目標(biāo)跟蹤問題求解框架，給出了分層遞階分布式主動求解結(jié)構(gòu)框圖，并做了簡單說明。UAV目標(biāo)跟蹤過程中存在諸多約束，包括無人機(jī)運(yùn)動學(xué)約束、傳感器測量約束、通信拓?fù)渥兓约癠AV飛行空域約束等。本文針對這些約束問題進(jìn)行了分析和建模，為后續(xù)UAV目標(biāo)狀態(tài)融合估計和UAV觀測軌跡生成求解過程提供了數(shù)學(xué)基礎(chǔ)。 其次，針對“智能”反跟蹤地面機(jī)動目標(biāo)的狀態(tài)融合估計方法進(jìn)行了研究。UAV通過對目標(biāo)的觀測信息估計目標(biāo)的運(yùn)動狀態(tài)是執(zhí)行跟蹤任務(wù)的基本條件，尤其對于機(jī)動性強(qiáng)的目標(biāo)而言，對目標(biāo)狀態(tài)的有效估計是UAV實時跟蹤目標(biāo)的關(guān)鍵因素。本文在分析擴(kuò)展卡爾曼濾波器（Extended Kalman Filter, EKF）和無跡信息濾波器（UnscentedInformation Filter, UIF）算法的基礎(chǔ)上，對最小最大化濾波器(Minimax Filter, MF)進(jìn)行擴(kuò)展，應(yīng)用于非線性目標(biāo)跟蹤過程中，并改進(jìn)提出了分布式一致性MF濾波器形式，，應(yīng)用于解決以下問題：1）被跟蹤目標(biāo)具有一定“智能”，即能夠進(jìn)行反跟蹤反監(jiān)視機(jī)動情況下，仍然可以對目標(biāo)狀態(tài)進(jìn)行持續(xù)估計；2）在通信拓?fù)渥兓瑴y量受限條件下的多UAV協(xié)同目標(biāo)狀態(tài)融合估計；3）多UAV分布式一致性目標(biāo)狀態(tài)融合估計。 最后，針對存在靜態(tài)障礙和動態(tài)威脅源的情況下，UAV對地面機(jī)動目標(biāo)的跟蹤航跡生成算法進(jìn)行了研究。UAV在飛行過程中受到空域限制，本文應(yīng)用李亞普諾夫?qū)Ш较蛄繄觯↙yapunov Guidance Vector Field,LGVF）引導(dǎo)UAV以指定對峙距離盤旋跟蹤地面目標(biāo)的基礎(chǔ)上，結(jié)合避碰勢場函數(shù)和相對速度空間的動態(tài)規(guī)劃方法，解決了UAV目標(biāo)跟蹤過程中飛行空域內(nèi)存在靜態(tài)障礙和動態(tài)威脅源時的避碰航跡規(guī)劃，持續(xù)對目標(biāo)運(yùn)動軌跡的跟蹤問題。
[Abstract]:With the development of unmanned aerial vehicle (UAV) application and technology, the cooperative target tracking problem of Unmanned Aerial vehicle (UAV), as a sub-task of other tasks, has been concerned and developed by many experts and scholars. In this paper, the tracking problem of multi-UAV cooperative ground maneuvering targets in complex environments is studied, focusing on the state fusion estimation and tracking of UAV maneuvering targets and track planning of UAV observations. First of all, the framework of multi-UAV cooperative target tracking problem in complex environment is analyzed, and the hierarchical distributed active solution structure block diagram is given, and some constraints in the process of target tracking are explained simply. Including UAV kinematics constraints, sensor measurement constraints, communication topology changes and UAV airspace constraints. In this paper, the constraint problems are analyzed and modeled, which provides a mathematical basis for the subsequent UAV target state fusion estimation and UAV observation trajectory generation process. Secondly, the state fusion estimation method of "intelligent" anti-tracking ground maneuvering target is studied. UAV estimates the moving state of the target through the observation information of the target is the basic condition of carrying out the tracking task. Especially for targets with strong maneuverability, the effective estimation of target state is the key factor for UAV to track targets in real time. Based on the analysis of extended Kalman filter (EKF) and Unscented Information filter (UIF) algorithm, this paper extends the minimum maximum filter (MF) and applies it to nonlinear target tracking. The distributed consistency MF filter is improved to solve the following problem: 1) the target is intelligent, that is, the target state can be continuously estimated under the condition of anti-tracking and anti-surveillance maneuver. 2) under the change of communication topology, the state fusion estimation of multi-UAV cooperative target under the condition of measurement constraints 3) multi-UAV distributed consistent target state fusion estimation. Finally, the tracking track generation algorithm of ground maneuvering target with UAV under the condition of static obstacle and dynamic threat source is studied. UAV is restricted by airspace during flight. In this paper, based on the Lyapunov guidance Vector LGVF (Lyapunov guidance Vector LGVF) to guide the UAV to determine the stand-off range hovering tracking ground target, the paper combines the collision avoidance potential field function and the dynamic programming method of relative velocity space. The collision avoidance trajectory planning is solved when there are static obstacles and dynamic threat sources in the flight airspace during UAV target tracking, and the problem of tracking the moving track of the target continuously is solved.
【學(xué)位授予單位】：北京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：V279;TN713

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