發(fā)布時間：2018-06-27 22:32

  本文選題：逆合成孔徑雷達(dá) + 相位誤差補(bǔ)償��； 參考：《西安電子科技大學(xué)》2016年博士論文

【摘要】：逆合成孔徑雷達(dá)(Inverse Synthetic Aperture Radar,ISAR)成像可獲得目標(biāo)的一維、二維和三維高分辨率成像結(jié)果,提供觀測目標(biāo)的尺寸和結(jié)構(gòu)等豐富的特征信息,它在空間態(tài)勢感知和防空反導(dǎo)等領(lǐng)域中發(fā)揮著重要的作用。經(jīng)過六十多年的長足發(fā)展,ISAR成像的基本理論較為成熟。但是,隨著ISAR系統(tǒng)的不斷發(fā)展和目標(biāo)觀測需求的不斷提高,ISAR二維和三維成像尚有不少問題亟待解決。首先,隨著當(dāng)前ISAR信號頻率向高頻段方向的不斷發(fā)展,雷達(dá)對發(fā)射及接收系統(tǒng)的相位噪聲和非線性影響更為敏感,將造成回波的距離維相位誤差,進(jìn)而降低目標(biāo)的ISAR成像質(zhì)量。其次,對于遠(yuǎn)作用距離和弱后向散射系數(shù)的目標(biāo)進(jìn)行觀測時,其回波的信噪比較低,傳統(tǒng)的ISAR成像方法難以完成精確的平動補(bǔ)償與成像。再次,對編隊飛行目標(biāo)的復(fù)雜觀測場景進(jìn)行成像時,子目標(biāo)相對雷達(dá)視線的運(yùn)動形式存在一定的差別,而其回波嚴(yán)重耦合,如何實現(xiàn)編隊飛行目標(biāo)良好的聚焦處理是目前雷達(dá)成像研究領(lǐng)域中的難題。同時,序列ISAR圖像包含了更多目標(biāo)的信息,如何基于序列ISAR圖像實現(xiàn)目標(biāo)的精確定標(biāo)和三維重構(gòu),是目前ISAR雷達(dá)成像研究領(lǐng)域中的挑戰(zhàn)性問題。上述這些問題不僅是雷達(dá)成像研究領(lǐng)域的難題,同時也是制約實際ISAR系統(tǒng)發(fā)展與應(yīng)用的技術(shù)瓶頸。因此,本文研究的ISAR二維與三維成像方法具有重要的理論意義和應(yīng)用價值。本文在國家自然科學(xué)基金、國家863課題與橫向課題等項目的資助下,結(jié)合實際ISAR系統(tǒng)的發(fā)展趨勢和復(fù)雜目標(biāo)觀測的迫切需求,針對ISAR距離維相位誤差補(bǔ)償、低信噪比下ISAR成像、多目標(biāo)ISAR成像、圖像定標(biāo)和目標(biāo)三維重構(gòu)等問題進(jìn)行了深入的研究,取得一些理論研究成果,并獲得聚焦良好的仿真與實測數(shù)據(jù)處理結(jié)果,進(jìn)而提升了ISAR的信息獲取能力。本文的具體內(nèi)容可分為以下六個部分：1.首先,對ISAR成像的基本原理進(jìn)行介紹,構(gòu)建了ISAR成像幾何模型和信號模型。然后,對ISAR成像處理過程中平動補(bǔ)償?shù)奈锢硪饬x和常用方法進(jìn)行了詳細(xì)的討論,闡述了越距離單元徙動(Migration Through Range Cells,MTRC)現(xiàn)象的產(chǎn)生原因、影響以及相應(yīng)的校正方法,為后文的二維與三維ISAR成像方法研究打下理論基礎(chǔ)。2.針對高波段寬帶ISAR的距離維非線性相位誤差嚴(yán)重影響目標(biāo)成像質(zhì)量的問題,提出了一種精確的基于最小熵與自聚焦處理的距離維相位誤差快速補(bǔ)償方法,獲得高精度的目標(biāo)距離維聚焦處理結(jié)果。首先,建立了ISAR回波距離維相位誤差的高階多項式模型,詳細(xì)分析了不同階次相位誤差對距離壓縮結(jié)果聚焦度的影響。然后,將距離壓縮結(jié)果的最小熵作為目標(biāo)函數(shù),利用啟發(fā)式搜索算法,對二階距離維相位誤差進(jìn)行準(zhǔn)確高效的估計和補(bǔ)償。最后,采用距離維自聚焦處理以準(zhǔn)確估計和校正三階及三階以上的高次距離維相位誤差,以獲得高精度的距離維相位誤差補(bǔ)償結(jié)果。仿真數(shù)據(jù)處理結(jié)果驗證了距離維相位誤差對ISAR目標(biāo)成像聚焦度的影響和本章所提出方法的有效性。3.針對低信噪比下傳統(tǒng)ISAR成像方法難以精確平動補(bǔ)償與成像的難題,提出了一種基于頻譜信號相位求導(dǎo)和粒子群優(yōu)化的自適應(yīng)的低信噪比下ISAR平動補(bǔ)償與成像方法,獲得了低信噪比情況下聚焦良好的ISAR實測數(shù)據(jù)成像結(jié)果。首先,結(jié)合相干的ISAR原始雷達(dá)回波,建立了目標(biāo)平動分量的高階多項式模型,可有效對不同的目標(biāo)平動形式進(jìn)行表示。接著,基于距離維回波頻譜相位求導(dǎo)提出了一種目標(biāo)平動分量多項式系數(shù)的粗估計方法,并將其結(jié)果作為后續(xù)利用粒子群優(yōu)化進(jìn)行平動分量精確估計的參考初始值,以提高運(yùn)算效率。然后,利用粒子群優(yōu)化(Particle Swarm Optimization, PSO)算法對目標(biāo)平動分量進(jìn)行精確估計,該算法實現(xiàn)簡便,有計算效率和估計精度高等優(yōu)點。最后,分析成像結(jié)果聚焦度隨目標(biāo)平動分量重構(gòu)精度的變化特性,以自適應(yīng)地確定目標(biāo)平動分量多項式模型的階數(shù),因此,本章所提出方法的通用性更強(qiáng)。仿真和實測數(shù)據(jù)處理結(jié)果驗證了本章所提出方法的有效性。4.針對編隊飛行目標(biāo)等復(fù)雜觀測場景中不同子目標(biāo)回波嚴(yán)重耦合的難題,提出了基于粒子群優(yōu)化和改進(jìn)CLEAN算法的多目標(biāo)ISAR成像方法。首先,針對傳統(tǒng)多目標(biāo)ISAR成像算法將不同目標(biāo)的運(yùn)動限定為基本一致或者完全不同的局限性,將多目標(biāo)劃分為多個組目標(biāo),同一組目標(biāo)內(nèi)不同子目標(biāo)的運(yùn)動形式和參數(shù)是一致的,該模型的通用性更強(qiáng)。其次,構(gòu)建組目標(biāo)平動分量的多項式模型,迭代利用PSO算法對不同組目標(biāo)的平動分量進(jìn)行準(zhǔn)確的估計和補(bǔ)償,得到不同組目標(biāo)的粗聚焦成像結(jié)果。然后,提出了改進(jìn)CLEAN算法對該組目標(biāo)成像結(jié)果進(jìn)行提取,在保留被提取組目標(biāo)圖像完整性的同時,對虛假點和噪聲進(jìn)行了更有效的壓制。最后,利用聚類數(shù)估計和K-均值算法對同一組目標(biāo)內(nèi)的子目標(biāo)進(jìn)行分割和提取,進(jìn)而通過單目標(biāo)成像算法逐個得到每個子目標(biāo)的良好聚焦成像結(jié)果。仿真實驗結(jié)果驗證了本章所提出方法的有效性。5.針對ISAR成像處理的橫向定標(biāo)難題,基于目標(biāo)等效旋轉(zhuǎn)角速度與ISAR散射點回波二次相位系數(shù)的解析關(guān)系,提出了一種基于離散多項式相位變換的ISAR圖像定標(biāo)方法,獲得高精度的Yak42飛機(jī)實測數(shù)據(jù)定標(biāo)處理結(jié)果。首先,對距離單元回波進(jìn)行了深入的分析,詳細(xì)推導(dǎo)了目標(biāo)等效轉(zhuǎn)動角速度與散射點二次相位系數(shù)的解析關(guān)系。其次,計算不同距離單元回波的歸一化幅度方差,并選取值較小的距離單元。接著,利用頻域加窗技術(shù)對上述距離單元中的強(qiáng)散射點頻譜進(jìn)行提取,同時給出了窗長的自適應(yīng)確定方法。之后,提出采用二階離散多項式相位變換(Discrete Polynomial-Phase Transform, DPT)對強(qiáng)散射點回波中的二次相位系數(shù)進(jìn)行估計,相比傳統(tǒng)基于最大對比度搜索和尺度Radon-Winger變換(Radon-Wigner Transform, RWT)的方法,該方法精度和計算效率較高,且實現(xiàn)簡單。最后,利用最小均方誤差(Least Square Error,LSE)估計得到目標(biāo)的等效轉(zhuǎn)動角速度,實現(xiàn)ISAR圖像精確的方位定標(biāo)。仿真和實測數(shù)據(jù)的定標(biāo)結(jié)果驗證了本章所提出方法的有效性。6.針對現(xiàn)有的基于序列ISAR圖像的目標(biāo)三維重構(gòu)算法存在尺度模糊和無法定標(biāo)的問題,提出了一種基于松弛約束分解的目標(biāo)三維重構(gòu)和圖像定標(biāo)聯(lián)合處理方法,其性能優(yōu)于現(xiàn)有的基于因式分解的三維重構(gòu)方法。首先,構(gòu)建了ISAR目標(biāo)的三維運(yùn)動和成像模型,并詳細(xì)推導(dǎo)了目標(biāo)三維結(jié)構(gòu)向成像平面的投影方程解析表達(dá)。之后,將大轉(zhuǎn)角的ISAR回波分段處理,以降低后續(xù)成像處理的難度,得到未定標(biāo)的序列ISAR圖像,進(jìn)而得到目標(biāo)散射點的航跡矩陣。然后,提出了改進(jìn)的因式分解方法,并利用其對散射點航跡矩陣進(jìn)行分解,該方法有效避免了航跡矩陣中散射點未定標(biāo)的方位位置對三維重構(gòu)精度的影響。接著,基于重構(gòu)的投影向量,通過最小均方誤差估計方法對目標(biāo)等效轉(zhuǎn)動角速度進(jìn)行估計,并重新對散射點的航跡矩陣進(jìn)行方位尺度校正。最后,迭代利用松弛約束的因式分解方法和目標(biāo)等效轉(zhuǎn)動角速度估計方法來提高算法精度,進(jìn)而聯(lián)合實現(xiàn)目標(biāo)三維結(jié)構(gòu)的重構(gòu)和ISAR圖像的定標(biāo)。仿真數(shù)據(jù)處理結(jié)果表明了本章所提出方法的性能優(yōu)于現(xiàn)有的基于因式分解的三維重構(gòu)方法。
[Abstract]:Inverse Synthetic Aperture Radar (ISAR) imaging can obtain one dimensional, two-dimensional and three-dimensional high-resolution imaging results of the target, providing rich feature information such as the size and structure of the observation target. It plays an important role in space situational awareness and air defense and antimissile. After more than 60 years of rapid development, The basic theory of ISAR imaging is more mature. However, with the continuous development of the ISAR system and the continuous improvement of the target observation demand, there are still many problems to be solved in ISAR and 3D imaging. First, with the continuous development of the current ISAR signal frequency to the high frequency section, the phase noise and nonlinearity of the emitter and receiving system The effect is more sensitive, which will cause the distance dimension phase error of the echo, and then reduce the ISAR imaging quality of the target. Secondly, when the target of the distance and the backward scattering coefficient is observed, the signal to noise of the echo is low, and the traditional ISAR imaging method is difficult to complete the accurate translational compensation and imaging. Again, the formation flight target is made. When the complex observation scene is imaging, there is a certain difference in the motion form of the sub target relative to the radar line of sight, and the echo is seriously coupled. How to realize the good focus processing of the formation flight target is a difficult problem in the field of radar imaging research. At the same time, the sequence ISAR image contains more information of the target, and how to base on the sequence ISAR It is a challenging problem in the field of ISAR radar imaging to realize the accurate target calibration and 3D reconstruction of the target. These problems are not only a difficult problem in the field of radar imaging research, but also a technical bottleneck restricting the development and application of the actual ISAR system. Therefore, the two dimensional and three-dimensional imaging methods of this paper are important for the research of this paper. Under the support of the National Natural Science Foundation, the National 863 subject and the horizontal project, this paper combines the development trend of the actual ISAR system and the urgent needs of the complex target observation, aiming at the ISAR distance dimension phase error compensation, the low signal to noise ratio ISAR imaging, the multi target ISAR imaging, the image calibration and the target. Some theoretical research results are carried out, some theoretical research results are obtained, and the results of good simulation and actual data processing are obtained. The information acquisition ability of ISAR can be improved. The specific content of this paper can be divided into six parts: 1. first, the basic principle of ISAR imaging is introduced, and the geometry of ISAR imaging geometry is constructed. Then, the physical meaning and common methods of the translational compensation in the ISAR imaging process are discussed in detail. The causes of the Migration Through Range Cells (MTRC) phenomenon, the influence and the corresponding correction method are expounded, and the two-dimensional and three-dimensional ISAR imaging methods of the later text are studied. In view of the problem that the target imaging quality is seriously affected by the distance dimension nonlinear phase error of the high wave band wideband ISAR, a fast compensation method of distance dimension phase error based on the minimum entropy and autofocus processing is proposed, and the high precision target distance dimension focusing processing results are obtained. First, the ISAR echo distance is established. In the high order polynomial model of phase error, the influence of different order phase errors on the focus of distance compression is analyzed in detail. Then, the minimum entropy of the distance compression result is used as the objective function, and the two order distance dimension phase error is accurately and efficiently estimated and compensated by the heuristic search algorithm. Finally, the distance dimension is used. The self focusing process is used to accurately estimate and correct the high order distance dimension phase error of the three order and above three order to obtain the high precision distance dimension phase error compensation results. The simulation data processing results verify the effect of the distance dimension phase error on the imaging focusing degree of the ISAR target and the validity of the method proposed in this chapter under the low signal to noise ratio. The traditional ISAR imaging method is difficult for accurate translation compensation and imaging. A ISAR translational compensation and imaging method based on the phase guidance of the spectrum signal and particle swarm optimization is proposed in the adaptive low signal to noise ratio (SNR). The imaging results of the ISAR measured data with good focus in the case of low signal to noise ratio are obtained. First, the coherent ISAR original mine is combined. The high order polynomial model of the target translational component is established, and the different target translational forms can be effectively expressed. Then, a rough estimation method for the polynomial coefficients of the target translational component is proposed based on the spectral phase derivation of the range dimension echo spectrum, and the result is made for the subsequent use of particle swarm optimization to make the translation component accurate. The estimated reference initial value is used to improve the operational efficiency. Then, the Particle Swarm Optimization (PSO) algorithm is used to accurately estimate the target translational components. The algorithm is simple to implement, has the advantages of high computational efficiency and high estimation precision. Finally, the changes of the imaging results focusing degree vary with the accuracy of the target translation component. In order to determine the order of the polynomial model of the target translation component adaptively, the method proposed in this chapter is more versatile. The simulation and measured data processing results verify that the effectiveness of the method proposed in this chapter.4. is based on the difficult problem of the serious coupling of the different sub targets in the complex observation scenes such as the formation flying targets. The multi-objective ISAR imaging method of particle swarm optimization and CLEAN algorithm is improved. Firstly, the traditional multi-objective ISAR imaging algorithm defines the motion of different targets as basically consistent or completely different limitations, and divides the multi target into multiple group targets, and the motion forms and parameters of the different subtargets in the same target are the same, the model is the same. Secondly, the polynomial model of the target translation component of the group is constructed, and the PSO algorithm is used to accurately estimate and compensate the translational components of different groups of targets, and the results of the rough focusing imaging of different groups of targets are obtained. Then, the improved CLEAN algorithm is proposed to extract the target imaging results of the group and be extracted and extracted. With the integrity of the target image, the false points and noise are more effectively suppressed. Finally, the clustering number estimation and K- mean algorithm are used to segment and extract the sub targets in the same set of targets, and then the good focusing imaging results of each sub target are obtained by single target imaging algorithm. The simulation results verify the results. The validity of the method proposed in this chapter.5. is based on the analytic relationship between the two phase coefficients of the target equivalent rotation angular velocity and the ISAR scattering point echo on the basis of the analytic relationship between the two phase coefficients of the target equivalent rotation angular velocity and the scattering point echo of the ISAR. A ISAR image calibration method based on the discrete polynomial phase transformation is proposed to obtain the high precision calibration of the measured data of the Yak42 aircraft. First, the echo of the distance unit is deeply analyzed. The analytic relation between the target equivalent rotation angular velocity and the two phase coefficient of the scattering point is derived in detail. Secondly, the normalized amplitude variance of the echo of different distance units is calculated and the distance unit with a smaller value is selected. Then, the range unit is used in the frequency domain to add the window technique to the distance unit. The strong scattering point spectrum is extracted and an adaptive method for determining the length of the window is given. After that, the two order discrete polynomial phase transformation (Discrete Polynomial-Phase Transform, DPT) is used to estimate the two phase coefficients of the strong scattering point echo, compared with the traditional maximum contrast search and the scale Radon-Winger transformation (Rad). The method of on-Wigner Transform, RWT) has high accuracy and efficiency, and it is simple to realize. Finally, the equivalent rotation angular velocity of the target is obtained by using the minimum mean square error (Least Square Error, LSE) to realize the accurate azimuth calibration of the ISAR image. The simulation and the calibration results of the real data verify the effectiveness of the method proposed in this chapter. In view of the problem that the existing target 3D reconstruction algorithm based on the sequence ISAR image exists the problem of scale fuzzy and uncalibrated, a method of joint processing of target 3D reconstruction and image calibration based on relaxation constraint decomposition is proposed. The performance of.6. is superior to the existing 3D reconstruction method based on factorization. First, the ISAR target is constructed. The three-dimensional motion and imaging model of the target are described in detail, and the projection equation of the three-dimensional structure of the target to the imaging plane is derived in detail. After that, the ISAR echo of the large rotation angle is segmented to reduce the difficulty of the subsequent imaging processing, and the unfixed sequence ISAR image is obtained, and then the track matrix of the target scattering point is obtained. Then, the improved cause is proposed. The method of decomposition is used to decompose the scattering point track matrix. This method effectively avoids the influence of the azimuth position of the scattering point in the track matrix on the three-dimensional reconstruction accuracy. Then, based on the reconstructed projection vector, the minimum mean square error estimation method is used to estimate the equivalent rotational angular velocity of the target, and the dispersion is rearranged. The path matrix of the point is corrected for azimuth scale. Finally, the algorithm is iteratively used to improve the accuracy of the algorithm and the target equivalent rotation angular velocity estimation method, and then the reconstruction of the three-dimensional structure of the target and the calibration of the ISAR image are combined. The results of the simulation data processing show that the performance of the proposed method is superior to that of this chapter. The existing three-dimensional reconstruction method based on factorization.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TN958

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