距离多普勒空间中的运动建模与状态估计

发布时间：2021-06-10 07:05

　　在现代雷达的发展过程中,其核心目的都是为了更多的提供目标信息,而一个可识别的目标通常意味着更多有用信息的获取。针对一个目标而言,获取其位置及空间信息更为重要。对某一目标的多重角度的信息获取是雷达一个重要的应用范围。高频表面波雷达（HFSWR）在海上使用时具有两个明显的优势:首先,通过垂直极化方式（HFSWR）可以观测到地平线以外,其次,它们的信号与海浪的相互作用机制很简单,也很容易理解。使用垂直极化的高频表面波雷达（HFSWR）可以区分和跟踪超出地平线的目标。因为HE的波长范围在10—100米,为了实现窄波束,天线阵列的孔径尺寸也需要很大,通常是几百甚至几千米。在发射和处理并行传输的信号过程中,传统的（HFSWR）大多都使用接收元件的天线系统来确定信号,为确保性能,传统的雷达依赖于窄波束,即在HF处的“窄光束”,其波长跨越10-100米,需要阵列的孔径尺寸在100米到1.5千米之间。同时,必须将电缆馈送到所有这些元件,并且温度和湿度的差异线参数需要构成偏置源,这就使得（HFSWR）在应用时具有很多限制条件,阻碍其性能增益。本文的研究重点是在只使用具有多个非方位角传感器进行多普勒测量时,... 

【文章来源】：哈尔滨工业大学黑龙江省 211工程院校 985工程院校

【文章页数】：67 页

【学位级别】：硕士

【文章目录】：
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


【参考文献】：
期刊论文
[1]极坐标系中带多普勒量测的雷达目标跟踪[J]. 段战胜,韩崇昭.  系统仿真学报. 2004(12)



本文编号：3221923