基于多分量三次相位信号的机动目标ISAR成像

发布时间：2021-03-02 12:21

　　雷达成像技术的快速发展为如何准确地观察目标提供了有效的方法。逆合成孔径雷达（ISAR）系统是一种常见的雷达成像方法,它是雷达信号处理领域的一种有效工具,用于获取非均匀旋转目标的聚焦图像。然而,由于时变多普勒参数的影响,ISAR成像技术一直面临挑战;ISAR成像质量与目标本身的运动特性有关。在运动补偿转换后,用多分量多项式相位信号（m-PPS）来表示回波的方位向信息,ISAR聚焦图像可以利用距离——瞬时多普勒（RID）技术结合mPPS的参数估计来获得。本文重点研究了基于多分量三次相位信号（m-CPS）的高机动目标ISAR成像技术。m-CPS的参数估计的准确性对成像质量有着非常重要的影响。因此,本文的主要目的是提出m-CPS的参数估计新方法,来提高目标的ISAR成像质量。本文的主要研究内容包括以下几个方面:1、通过改进传统的离散多项式变换（DPT）方法,本文提出了一种基于修正离散多项式变换（MDPT）的m-CPS信号参数估计方法。可实现三次相位信号的参数估计。本文通过真实目标的成像结果来说明这种算法的有效性。2、本文提出了一种修正乘积型高阶模糊度函数（IPHAF）的算法来估计mCPS信号的... 

【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】：117 页

【学位级别】：博士

【文章目录】：
摘要
Abstract
Abbreviations
Chapter 1 Introduction
    1.1 Research background and significance
    1.2 Outline of ISAR system development
        1.2.1 Land-based ISAR imaging
        1.2.2 Airborne ISAR system
        1.2.3 Shipborne ISAR system
        1.2.4 Spaceborne ISAR imaging （SISAR）
    1.3 Review of ISAR Imaging method
        1.3.1 Range compression
        1.3.2 Motion compensation
        1.3.3 ISAR imaging geometry and Signal model construction
        1.3.4 ISAR image resolution
    1.4 Main research contents of this thesis
    1.5 Structure of the Thesis
Chapter 2 ISAR imaging based on MDPT
    2.1 Introduction
    2.2 Signal model construction
    2.3 Parameters estimation of multi-component CPS based on MDPT
        2.3.1 The definition of DPT
        2.3.2 Parameters estimation of multi-component CPS based on MDPT
    2.4 Numerical examples
    2.5 ISAR imaging algorithm based on MDPT
    2.6 ISAR imaging results
        2.6.1 ISAR imaging result of simulated data
        2.6.2 ISAR imaging result of real aircraft data
    2.7 Summary
Chapter 3 Imaging of high-speed manoeuvering target via IPHAF algorithm .
    3.1 Introduction
    3.2 Parameters estimation of the m-CPS signal model based on IPHAF algorithm
        3.2.1 Brief review of High-order Ambiguity Function （HAF）
        3.2.2 Brief review of Product High-order Ambiguity Function （PHAF）
        3.2.3 Improved-version of Product High-order Ambiguity Function （IPHAF）
        3.2.4 Estimation of the rest parameters for m-CPS signal
    3.3 Numerical examples
        3.3.1 Mono-component CPS signal
        3.3.2 Multiple CPS signals
        3.3.3 Comparison with some existing methods
    3.4 ISAR imaging of maneuvering target based on IPHAF algorithm
    3.5 Experimental Results
        3.5.1 ISAR imaging result of simulated data
        3.5.2 ISAR imaging result of real aircraft data
    3.6 Summary
Chapter 4 Imaging of Target with Complicated Motion using ISAR System based on IPHAF-TVA
    4.1 Introduction
    4.2 Cubic Phase Signal model with TVA
    4.3 Parameters estimation of m-CPS based on IPHAF-TVA algorithm
        4.3.1 The phase parameters estimation
        4.3.2 The amplitude estimation
    4.4 Numerical Examples for CPS signal with TVA
        4.4.1 One-component CPS signal with TVA
        4.4.2 Two-component CPS signal TVA
    4.5 ISAR imaging algorithm based on m-CPS signal model with TVA
    4.6 Experimental Results
        4.6.1 ISAR imaging result of simulated data
        4.6.2 ISAR imaging result of real aircraft data
    4.7 Summary
Chapter 5 ISAR Imaging Algorithm Based on MCPF and Special Narrow Spectrum Filter
    5.1 Introduction
    5.2 Signal model for the ISAR received signal
    5.3 Parameters estimation of the m-CPS signal model
        5.3.1 The phase coefficients estimation using the MCPF technique
        5.3.2 The estimation of the amplitude
    5.4 ISAR imaging algorithm based on the m-CPS signal model with TVA
    5.5 Experimental Results
        5.5.1 Simulation of aircraft model points
        5.5.2 Simulation of ship model points
        5.5.3 ISAR imaging result of real aircraft data
    5.6 Summary
Conclusion
References
List of Publications
Acknowledgements


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期刊论文
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