無人機(jī)是一種由動力驅(qū)動、機(jī)上無人駕駛、可重復(fù)使用的航空飛行器,在軍事和民用領(lǐng)域都得到了廣泛的應(yīng)用。在近年來的幾次局部戰(zhàn)爭中,無人機(jī)有效的執(zhí)行了包括照相偵查、信號情報搜索、戰(zhàn)場損傷評估在內(nèi)的多種軍事任務(wù),作為軍隊(duì)?wèi)?zhàn)斗力的倍增器受到各國軍方的普遍關(guān)注,并成為科學(xué)研究的熱點(diǎn)。在美軍的網(wǎng)絡(luò)中心戰(zhàn)理論中,無人機(jī)以其良好的戰(zhàn)場感知能力成為了重要環(huán)節(jié)。在未來無人機(jī)的發(fā)展在不斷追求高性能的過程中,對導(dǎo)航技術(shù)的要求也必將越來越高,不斷追求精度更高、效率更高、準(zhǔn)確性及魯棒性更高的導(dǎo)航算法,以滿足未來無人機(jī)發(fā)展的需要。由于慣性導(dǎo)航系統(tǒng)（INS）導(dǎo)航輸出數(shù)據(jù)平穩(wěn),短期穩(wěn)定性好,具有極強(qiáng)的自主性,使其成為了無人機(jī)導(dǎo)航領(lǐng)域中最為核心的導(dǎo)航方式,但是慣導(dǎo)系統(tǒng)的誤差會隨時間累積。衛(wèi)星導(dǎo)航系統(tǒng)易受環(huán)境和載體運(yùn)動影響而丟失信號,但長期穩(wěn)定性好,是目前最為常用的用來輔助慣性導(dǎo)航的輔助系統(tǒng)。本文利用INS和GPS進(jìn)行組合導(dǎo)航,既能夠利用慣性導(dǎo)航的自身優(yōu)勢,又能通過GPS避免慣性導(dǎo)航中系統(tǒng)誤差與隨機(jī)誤差帶來的導(dǎo)航參數(shù)誤差。為進(jìn)行組合導(dǎo)航系統(tǒng)建模,本文首先定義了導(dǎo)航坐標(biāo)系,建立了慣性導(dǎo)航系統(tǒng)的數(shù)學(xué)模型,給出了比力和角速度的數(shù)學(xué)模... 

【文章來源】：哈爾濱工業(yè)大學(xué)黑龍江省 211工程院校 985工程院校

【文章頁數(shù)】：68 頁

【學(xué)位級別】：碩士

【文章目錄】：
Abstract
摘要
Acronyms
Chapter 1. Introduction
    1.1. Background Introduction
        1.1.1. Components of UAV
        1.1.2. Instruments and Sensors in UAVS
        1.1.3. Used of INS/GPS in UAVS
    1.2. Motivation
    1.3. Research Contribution
    1.4. Thesis Overview
Chapter 2. Literature review
    2.1. Overview
    2.2. Inertial Navigation (INS)
        2.2.1. Principle of inertial navigation (INS)
        2.2.2. Function of INS
        2.2.3. Advantages of INS
        2.2.4. Disadvantages of INS
    2.3. GPS
        2.3.1. Basic Concept of GPS
        2.3.2. Function of GPS
        2.3.3. Using GPS for Tracking and its Applications
        2.3.4. GPS Strengths and Weaknesses
    2.4. INS/GPS
        2.4.1. INS/GPS Method
        2.4.2. Applications
    2.5. Kalman Filter
    2.6. Extended Kalman Filter
    2.7. Cubature Kalman Filter & Huber based high Degree Cubature Kalman Filter
Chapter 3. Research Methodology
    3.1. Overview
    3.2. Inertial Navigation System (INS)
    3.3. Global Positioning System (GPS) Principle of GPS:
    3.4. Extended Kalman filter (EKF)
    3.5. Cubature Kalman Filter & Huber Based Cubature Kalman Filter
        3.5.1. Time Update Equations
        3.5.2. Huber's Technique used for Measurement Update
Chapter 4. Results and Discussion
    4.1. Overview
    4.2. Simulation results
        4.2.1. Inertial Navigation system results:
        4.2.2. GPS Simulation results:
        4.2.3. Extended Kalman Filter
        4.2.4. Cubature Kalman filter
        4.2.5. Huber based high Degree Cubature Kalman Filter
    4.3. Performance analysis
        4.3.1. Comparison of Velocity error
        4.3.2. Comparison of Position error
Conclusion
References
Acknowledgements
Resume



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