發(fā)布時間：2021-06-10 07:05

　　在現(xiàn)代雷達的發(fā)展過程中,其核心目的都是為了更多的提供目標(biāo)信息,而一個可識別的目標(biāo)通常意味著更多有用信息的獲取。針對一個目標(biāo)而言,獲取其位置及空間信息更為重要。對某一目標(biāo)的多重角度的信息獲取是雷達一個重要的應(yīng)用范圍。高頻表面波雷達（HFSWR）在海上使用時具有兩個明顯的優(yōu)勢:首先,通過垂直極化方式（HFSWR）可以觀測到地平線以外,其次,它們的信號與海浪的相互作用機制很簡單,也很容易理解。使用垂直極化的高頻表面波雷達（HFSWR）可以區(qū)分和跟蹤超出地平線的目標(biāo)。因為HE的波長范圍在10—100米,為了實現(xiàn)窄波束,天線陣列的孔徑尺寸也需要很大,通常是幾百甚至幾千米。在發(fā)射和處理并行傳輸?shù)男盘栠^程中,傳統(tǒng)的（HFSWR）大多都使用接收元件的天線系統(tǒng)來確定信號,為確保性能,傳統(tǒng)的雷達依賴于窄波束,即在HF處的“窄光束”,其波長跨越10-100米,需要陣列的孔徑尺寸在100米到1.5千米之間。同時,必須將電纜饋送到所有這些元件,并且溫度和濕度的差異線參數(shù)需要構(gòu)成偏置源,這就使得（HFSWR）在應(yīng)用時具有很多限制條件,阻礙其性能增益。本文的研究重點是在只使用具有多個非方位角傳感器進行多普勒測量時,... 

【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)】：67 頁

【學(xué)位級別】：碩士

【文章目錄】：
Abstract
摘要
Chapter 1. Introduction
    1.1 Background Introduction
    1.2 Motivation
    1.3 Research Purpose and Significance
    1.4 Research Contribution
    1.5 Thesis Overview
Chapter 2. Fundamental Principle of Kalman Filters
    2.1 Overview
    2.2 Kalman Filter and its Relatives
        2.2.1 The Fundamental Thought of a Kalman Filter
    2.3 Kalman Filter
    2.4 Extended Kalman Filter
    2.5 Unscented Kalman Filter (UKF)
    2.6 Nonlinear Observation/Output Equations
    2.7 Other Nonlinear Kalman Filtering Techniques
    2.8 Summary
Chapter 3. Research Methodology
    3.1 Overview
    3.2 Implementation of a Kalman Filter
        3.2.1 Filtering Problem Definition
    3.3 Kalman Filtering Algorithm
    3.4 Continuous vs. Discrete Time Kalman Filters
    3.5 Effect of Noise Covariance Assumptions
    3.6 Unscented Kalman filter UKF
    3.7 Some Practical Implementation of Kalman filters
        3.7.1 One-dimension tracking performance
        3.7.2 Preliminary Results
        3.7.3 Two-dimension tracking performance
        3.7.4 Preliminary Results
        3.7.5 Root Mean Square Error (RMSE)
    3.8 Summary
Chapter 4. Converted Doppler Measurement Kalman Filter (CDMKF)
    4.1 Overview
    4.2 Basics of Converted Doppler Measurement Kalman Filter
    4.3 System Formulation
    4.4 Measurement Conversion Equations
    4.5 Converted Measurement Errors
    4.6 Converted Doppler Measurement Kalman Filter CV Model
        4.6.1 Pseudo state Equations
        4.6.2 Derivation of the CDMKF
    4.7 Preliminary Results for CDMKF
    4.8 Summary
Chapter 5. Principle of Rang-Doppler Modeling and Filter
    5.1 Overview
    5.2 Importance of Modeling and Rang-Doppler Modeling
    5.3 Principle of Rang-Doppler Modeling
    5.4 The Filter Model
    5.5 Filter Initialization
    5.6 Simulation Tool
    5.7 Results Discussion
    5.8 Summary
Conclusion
References
Acknowledgements


【參考文獻】：
期刊論文
[1]極坐標(biāo)系中帶多普勒量測的雷達目標(biāo)跟蹤[J]. 段戰(zhàn)勝,韓崇昭.  系統(tǒng)仿真學(xué)報. 2004(12)



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