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  本文選題：彈道導(dǎo)彈防御 + 窄帶雷達(dá)��； 參考：《國防科學(xué)技術(shù)大學(xué)》2014年博士論文

【摘要】：深入挖掘窄帶雷達(dá)潛力,研究基于窄帶信息的彈道中段目標(biāo)識別和特征提取技術(shù)對于彈道中段攻防對抗具有重要意義。本文以彈道導(dǎo)彈防御為背景,針對彈道中段目標(biāo)識別難題,重點(diǎn)研究了基于軌道特征的有源假目標(biāo)識別技術(shù)、基于窄帶RCS(Radar Cross Section,RCS)序列的進(jìn)動目標(biāo)周期特征提取技術(shù)和基于時頻變換域的進(jìn)動目標(biāo)成像技術(shù)。第一章闡述了課題研究背景及意義,簡要介紹了彈道導(dǎo)彈攻防對抗的現(xiàn)狀,對基于窄帶雷達(dá)信息的彈道中段目標(biāo)識別和特征提取相關(guān)技術(shù)進(jìn)行了歸納總結(jié)和分析,最后介紹了本文的主要研究工作。第二章為彈道目標(biāo)跟蹤基礎(chǔ)理論研究。首先介紹了跟蹤濾波的基本原理,對基于動力學(xué)模型和基于運(yùn)動學(xué)模型的彈道目標(biāo)跟蹤濾波的數(shù)學(xué)模型及關(guān)鍵要素進(jìn)行了分析和討論;然后從狀態(tài)方程的預(yù)測性能和濾波過程的線性化兩個方面,分別提出了基于運(yùn)動學(xué)與動力學(xué)混合模型的彈道中段目標(biāo)跟蹤算法和基于變換觀測量的雷達(dá)垂直坐標(biāo)系下的彈道中段目標(biāo)跟蹤算法。所提方法兼具動力學(xué)和運(yùn)動學(xué)兩種模型的優(yōu)點(diǎn),即既具有與動力學(xué)模型相近的性能,又具有運(yùn)動學(xué)模型線性化的結(jié)構(gòu)。第三章研究了基于彈道中段目標(biāo)軌道特征的有源假目標(biāo)識別技術(shù)。首先深入分析了彈道目標(biāo)和有源假目標(biāo)的動量矩、近拱點(diǎn)矢量和軌道根數(shù)等軌道特征量。在此基礎(chǔ)上,提出了基于軌道根數(shù)時不變特性的有源假目標(biāo)識別技術(shù),給出了時不變判定準(zhǔn)則,基于軌道可逆性原理實(shí)現(xiàn)了初始觀測數(shù)據(jù)的精確估計。最后對三種典型布站情況進(jìn)行了仿真,仿真結(jié)果驗(yàn)證了所提方法的有效性。第四章研究了基于窄帶RCS序列的彈道中段進(jìn)動目標(biāo)周期特征提取技術(shù)。首先利用電磁計算軟件計算得到典型中段目標(biāo)的RCS數(shù)據(jù),通過樣條擬合生成其動態(tài)RCS序列;簡要介紹了傳統(tǒng)的RCS特征提取方法,指出了傳統(tǒng)方法在基于RCS序列提取進(jìn)動目標(biāo)周期特征時的局限性。然后從周期的定義和性質(zhì)出發(fā),分別提出了基于分組數(shù)據(jù)相似性度量和基于分組數(shù)據(jù)非參數(shù)統(tǒng)計特征的周期估計方法,給出了周期判定準(zhǔn)則。仿真結(jié)果表明,所提方法能有效克服傳統(tǒng)方法的缺陷。第五章研究了基于時頻域的彈道中段進(jìn)動目標(biāo)窄帶成像技術(shù)。首先概述了傳統(tǒng)基于理想散射中心的進(jìn)動目標(biāo)窄帶回波建模方法、時頻分析技術(shù)和基于時頻變換域的窄帶成像方法,通過仿真分析指出了當(dāng)存在非理想散射現(xiàn)象時傳統(tǒng)成像方法的不足。然后建立了基于非理想散射中心的彈道中段進(jìn)動目標(biāo)窄帶回波模型,提出在時頻變換之前基于經(jīng)驗(yàn)?zāi)B(tài)分解對信號進(jìn)行分解,然后對各本征模態(tài)分別進(jìn)行時頻變換域成像的方法。最后通過暗室測量數(shù)據(jù)和暗室進(jìn)動實(shí)驗(yàn)分別驗(yàn)證了模型的正確性和所提成像方法的有效性。第六章總結(jié)了論文的研究工作和主要創(chuàng)新點(diǎn),指出需要進(jìn)一步研究的問題。
[Abstract]:In order to exploit the potential of narrowband radar, it is very important to study the target recognition and feature extraction technology based on narrowband information. In this paper, based on ballistic missile defense, aiming at the difficult problem of target recognition in the middle of trajectory, the active false target recognition technology based on orbit feature is studied. The precession target periodic feature extraction technique based on narrowband RCS(Radar Cross sequence and the precession target imaging technology based on time-frequency transform domain. The first chapter describes the research background and significance of the subject, briefly introduces the current situation of ballistic missile attack and defense countermeasures, summarizes and analyzes the related techniques of target recognition and feature extraction in the middle part of trajectory based on narrowband radar information. Finally, the main research work of this paper is introduced. The second chapter is the basic theory research of trajectory target tracking. Firstly, the basic principle of tracking filtering is introduced, and the mathematical model and key elements of trajectory target tracking filtering based on dynamics model and kinematics model are analyzed and discussed. Then, the prediction performance of the equation of state and the linearization of the filtering process are discussed. The algorithms of midcourse target tracking based on the mixed kinematics and dynamics model and the radar midcourse target tracking algorithm based on the transformed observations in the vertical coordinate system are proposed respectively. The proposed method has the advantages of both dynamics and kinematics, that is, it has not only the same performance as the dynamic model, but also the linearized structure of the kinematic model. In the third chapter, the active false target recognition technology based on the trajectory characteristics of the middle trajectory is studied. First, the orbital eigenvalues such as moment of momentum, near arch point vector and orbital root number of ballistic target and active false target are analyzed. On this basis, an active false target recognition technique based on the time-invariant property of orbital root number is proposed, and the criterion of time-invariant decision is given, and the accurate estimation of initial observation data is realized based on the principle of orbit reversibility. Finally, the simulation results of three typical stations are given to verify the effectiveness of the proposed method. In chapter 4, we study the method of extracting the periodic feature of the moving target in the middle of trajectory based on narrowband RCS sequence. At first, the RCS data of typical middle target are calculated by electromagnetic calculation software, and its dynamic RCS sequence is generated by spline fitting, and the traditional RCS feature extraction method is introduced briefly. The limitation of traditional method in extracting precession target periodic features based on RCS sequence is pointed out. Then, based on the definition and property of the period, the method of period estimation based on the similarity measure of packet data and the non-parametric statistical feature of packet data is proposed, and the cycle criterion is given. Simulation results show that the proposed method can effectively overcome the shortcomings of traditional methods. In chapter 5, the narrowband imaging technology of the moving target in the middle part of trajectory based on time-frequency domain is studied. Firstly, the traditional narrowband echo modeling method based on ideal scattering center, time-frequency analysis technique and narrow-band imaging method based on time-frequency transform domain are summarized. The shortcomings of traditional imaging methods are pointed out by simulation analysis when there are non-ideal scattering phenomena. Then the narrowband echo model of the precession target in the middle trajectory based on the non-ideal scattering center is established and the signal is decomposed based on the empirical mode decomposition before the time-frequency transformation. Then the time-frequency transform domain imaging method is used for each intrinsic mode. Finally, the correctness of the model and the validity of the proposed imaging method are verified by the anechoic measurement data and the anechoic precession experiment, respectively. Chapter 6 summarizes the research work and main innovation points, and points out the problems that need further research.
【學(xué)位授予單位】：國防科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TN957.52

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