雷達(dá)成像技術(shù)的快速發(fā)展為如何準(zhǔn)確地觀察目標(biāo)提供了有效的方法。逆合成孔徑雷達(dá)（ISAR）系統(tǒng)是一種常見的雷達(dá)成像方法,它是雷達(dá)信號處理領(lǐng)域的一種有效工具,用于獲取非均勻旋轉(zhuǎn)目標(biāo)的聚焦圖像。然而,由于時變多普勒參數(shù)的影響,ISAR成像技術(shù)一直面臨挑戰(zhàn);ISAR成像質(zhì)量與目標(biāo)本身的運(yùn)動特性有關(guān)。在運(yùn)動補(bǔ)償轉(zhuǎn)換后,用多分量多項(xiàng)式相位信號（m-PPS）來表示回波的方位向信息,ISAR聚焦圖像可以利用距離——瞬時多普勒（RID）技術(shù)結(jié)合mPPS的參數(shù)估計(jì)來獲得。本文重點(diǎn)研究了基于多分量三次相位信號（m-CPS）的高機(jī)動目標(biāo)ISAR成像技術(shù)。m-CPS的參數(shù)估計(jì)的準(zhǔn)確性對成像質(zhì)量有著非常重要的影響。因此,本文的主要目的是提出m-CPS的參數(shù)估計(jì)新方法,來提高目標(biāo)的ISAR成像質(zhì)量。本文的主要研究內(nèi)容包括以下幾個方面:1、通過改進(jìn)傳統(tǒng)的離散多項(xiàng)式變換（DPT）方法,本文提出了一種基于修正離散多項(xiàng)式變換（MDPT）的m-CPS信號參數(shù)估計(jì)方法�？蓪�(shí)現(xiàn)三次相位信號的參數(shù)估計(jì)。本文通過真實(shí)目標(biāo)的成像結(jié)果來說明這種算法的有效性。2、本文提出了一種修正乘積型高階模糊度函數(shù)（IPHAF）的算法來估計(jì)mCPS信號的... 

【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)】：117 頁

【學(xué)位級別】：博士

【文章目錄】：
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


【參考文獻(xiàn)】：
期刊論文
[1]低軌衛(wèi)星目標(biāo)干涉ISAR三維成像方法[J]. 曹星慧,宋慶雷,姜巖,徐國棟.  雷達(dá)科學(xué)與技術(shù). 2007(03)
[2]Detection and parameter estimation of multicomponent LFM signal based on the fractional Fourier transform[J]. QI Lin1, 2, TAO Ran1, ZHOU Siyong1 & WANG Yue1 1. Department of Electronic Engineering, Beijing Institute of Technology, Beijing 100081, China; 2. School of Information Engineering, Zhengzhou University, Zhengzhou 450052, China Correspondence should be addressed to QI Lin (email: qilin@bit.edu.cn).  Science in China（Series F:Information Sciences）. 2004(02)
[3]Two modified discrete chirp Fourier transform schemes[J]. 樊平毅,夏香根.  Science in China（Series F:Information Sciences）. 2001(05)



本文編號：3059251