本文關(guān)鍵詞：ISAR高分辨成像和參數(shù)估計(jì)算法研究　出處：《西安電子科技大學(xué)》2016年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 逆合成孔徑雷達(dá) 稀疏信號(hào)處理 稀疏孔徑 方位定標(biāo) 機(jī)動(dòng)目標(biāo)

【摘要】：隨著國(guó)民經(jīng)濟(jì)的提升和現(xiàn)代戰(zhàn)爭(zhēng)形式的轉(zhuǎn)變,雷達(dá)成像技術(shù)憑借其遠(yuǎn)距離、全天時(shí)、全天候高分辨成像的獨(dú)特優(yōu)勢(shì),在民用和國(guó)防遙感領(lǐng)域發(fā)揮著不可替代的作用。作為對(duì)空間、空中及海洋觀測(cè)最重要的手段之一,逆合成孔徑雷達(dá)(Inverse Synthetic Aperture Radar,ISAR)成像是非合作目標(biāo)識(shí)別的關(guān)鍵技術(shù)。為了滿足日益增加的應(yīng)用需求,ISAR正朝著多功能、多維度和協(xié)同網(wǎng)絡(luò)等方向發(fā)展。工作模式和數(shù)據(jù)獲取方式的多樣化,以及目標(biāo)運(yùn)動(dòng)的復(fù)雜性,使得現(xiàn)有的ISAR成像體制面臨著高分辨成像和目標(biāo)參數(shù)提取等挑戰(zhàn)。在國(guó)家“973”計(jì)劃課題等多個(gè)項(xiàng)目支持下,本文針對(duì)現(xiàn)有ISAR成像中存在的短孔徑低分辨成像、非相干的稀疏孔徑以及平穩(wěn)或機(jī)動(dòng)目標(biāo)定標(biāo)等問(wèn)題開(kāi)展研究,旨在增強(qiáng)ISAR圖像分辨率和探討穩(wěn)定的參數(shù)估計(jì)方法,以提高雷達(dá)自動(dòng)目標(biāo)識(shí)別能力。研究?jī)?nèi)容主要包括以下幾方面:(1)ISAR短孔徑數(shù)據(jù)的稀疏高分辨成像由于雷達(dá)的多模式工作狀態(tài)或目標(biāo)的機(jī)動(dòng)性,短孔徑數(shù)據(jù)在ISAR成像中普遍存在。盡管成像方法簡(jiǎn)單高效,但同時(shí)限制了圖像分辨率,影響目標(biāo)識(shí)別性能。在分析ISAR回波模型和典型運(yùn)動(dòng)補(bǔ)償算法的基礎(chǔ)上,本文第二章基于壓縮感知理論介紹了稀疏ISAR信號(hào)的重構(gòu)方法。該方法重點(diǎn)從統(tǒng)計(jì)的角度提出了一種基于數(shù)據(jù)的稀疏約束參數(shù)估計(jì)方法。通過(guò)推導(dǎo)重構(gòu)高分辨圖像的正則化問(wèn)題,稀疏約束參數(shù)可由最大似然估計(jì)得到解析形式。其中,噪聲方差由粗圖像中大量的噪聲單元估計(jì),權(quán)值由預(yù)處理的降噪圖像進(jìn)行初始化。利用優(yōu)化求解的高分辨圖像,再對(duì)稀疏約束參數(shù)進(jìn)行更新并重新估計(jì)圖像,多次循環(huán)提升算法性能。結(jié)果表明,迭代估計(jì)的稀疏約束參數(shù)能在重構(gòu)高分辨圖像過(guò)程中,較好地權(quán)衡信號(hào)逼真度和稀疏性。(2)稀疏孔徑的相干化處理和高分辨成像除了雷達(dá)多樣化的工作模式外,外界或系統(tǒng)的干擾也會(huì)造成數(shù)據(jù)的缺損,形成稀疏孔徑。對(duì)于塊狀稀疏分布的子孔徑,經(jīng)過(guò)獨(dú)立的包絡(luò)對(duì)齊和自聚焦處理后,殘余的線性相位和復(fù)幅度將在觀測(cè)孔徑之間存在差異。針對(duì)這種稀疏孔徑之間的非相干性,本文第三章提出了一種相干化處理方法。將子孔徑的包絡(luò)按質(zhì)心對(duì)齊后,對(duì)各子圖像中的特顯點(diǎn)單元建立全極點(diǎn)模型,并由求根MUSIC算法估計(jì)極點(diǎn),計(jì)算子孔徑間的多普勒偏移。以其中一個(gè)子孔徑作為基準(zhǔn)校正線性相位后,通過(guò)最小二乘(Least Square,LS)方法求解各子孔徑的模型系數(shù)。由估計(jì)的極點(diǎn)和系數(shù),將頻偏校準(zhǔn)后的子孔徑分別進(jìn)行前向和后向外推至整個(gè)孔徑長(zhǎng)度。再利用LS估計(jì)復(fù)幅度偏差并進(jìn)行校正。在相干化處理后,構(gòu)造部分FFT基作為觀測(cè)矩陣,通過(guò)稀疏信號(hào)處理的方法對(duì)缺失孔徑進(jìn)行恢復(fù)。從實(shí)驗(yàn)結(jié)果可看出,經(jīng)過(guò)相干化處理和空缺孔徑重構(gòu)后,成像效果得到了明顯提升。(3)勻速轉(zhuǎn)動(dòng)目標(biāo)的定標(biāo)方法目標(biāo)識(shí)別除了需要高分辨圖像之外,還需要精確的目標(biāo)尺寸參數(shù)。因此,本文的第四章和第五章針對(duì)勻速轉(zhuǎn)動(dòng)目標(biāo)提出了兩種不同的定標(biāo)方法。第四章先分析了平動(dòng)補(bǔ)償過(guò)程引入的殘余平動(dòng)相位對(duì)不同定標(biāo)方法的潛在影響。重點(diǎn)提出了一種利用散射點(diǎn)調(diào)頻率相消的有效轉(zhuǎn)動(dòng)速度(Effective Rotaional Velocity,ERV)估計(jì)方法,該方法幾乎不受殘余平動(dòng)的影響。通過(guò)選取散射點(diǎn)單元,并將其時(shí)域信號(hào)看作時(shí)變自回歸模型。對(duì)短時(shí)數(shù)據(jù)段估計(jì)瞬時(shí)極點(diǎn)后,滑窗獲得整個(gè)孔徑的多普勒歷程。根據(jù)關(guān)系式,ERV可以從不同距離單元的散射點(diǎn)調(diào)頻率解算。轉(zhuǎn)動(dòng)相位補(bǔ)償后,再進(jìn)行自聚焦處理提高圖像聚焦度。由于該方法依賴于提取的散射點(diǎn)質(zhì)量,在信噪比(Signal-to-noise ratio,SNR)較低時(shí)估計(jì)性能會(huì)下降。為了提高定標(biāo)算法的穩(wěn)定性,第五章針對(duì)勻速轉(zhuǎn)動(dòng)目標(biāo)提出了基于圖像整體性能的ERV估計(jì)方法。該方法考慮殘余平動(dòng)的影響,將其作為參數(shù)聯(lián)合ERV估計(jì)。通過(guò)迭代補(bǔ)償二次相位誤差,直到補(bǔ)償后的圖像幅度平方銳化度(Intensity-squared Sharpness,ISS)達(dá)到最大值,同時(shí)獲得聚焦圖像和ERV估計(jì)值。在此基礎(chǔ)上,再對(duì)聚焦圖像進(jìn)行自聚焦處理,進(jìn)一步聚焦圖像。其中,ISS最大化問(wèn)題是典型的非線性最小二乘問(wèn)題,利用高斯牛頓的方法可實(shí)現(xiàn)高效求解。與多種方法的對(duì)比結(jié)果說(shuō)明,這種從圖像性能角度估計(jì)ERV的方法雖然效率中等,但具有較高的精度和魯棒性。(4)非勻速轉(zhuǎn)動(dòng)目標(biāo)的定標(biāo)和高分辨成像方法由于勻加速轉(zhuǎn)動(dòng)引起的距離-方位二維耦合,增加了轉(zhuǎn)動(dòng)參數(shù)估計(jì)的難度。針對(duì)此類目標(biāo),本文第六章首先提出了一種基于匹配傅里葉變換(Matched Fourier Transform,MFT)的圖像ISS最大化的定標(biāo)方法。該方法將RD成像中FFT線性變換替換為參數(shù)化的MFT變換。通過(guò)對(duì)二維耦合相位迭代補(bǔ)償,使得MFT圖像的ISS最大,估計(jì)得到MFT調(diào)頻率和ERV參數(shù)。該定標(biāo)方法能在信噪比較低的條件下保持較好的估計(jì)性能。但強(qiáng)噪聲或低分辨率仍影響目標(biāo)識(shí)別,因此,利用部分MFT基和稀疏信號(hào)處理的方法進(jìn)一步完成了高分辨圖像的重構(gòu)。
[Abstract]:With the promotion of national economy and the transformation of modern warfare, radar imaging technology with its unique advantages of long distance, all day long, all-weather, high resolution imaging, plays an irreplaceable role in civilian and military fields. As for space remote sensing, air and ocean observation is one of the most important means of inverse synthetic aperture radar (Inverse Synthetic Aperture Radar, ISAR) imaging is the key technology of non cooperative target recognition. In order to meet the application demand increasing, ISAR is moving towards multi function, multi dimension and collaborative network development. The diversification of working mode and data acquisition, and the complexity of target movement, the ISAR imaging system of existing face a high resolution imaging and target extraction and other challenges. In the national "973" project and other projects supported by the existing short aperture ISAR imaging in low Sparse aperture resolution imaging, non coherent and stable or maneuvering target calibration and so on, in order to enhance the resolution of ISAR image parameters and discuss the stability estimation method to improve radar automatic target recognition. The main research contents include the following aspects: (1) ISAR sparse short aperture data of high resolution imaging radar due to mobility the multi mode state of target, short aperture data exists in ISAR imaging. Although the imaging method is simple and efficient, but also limits the resolution of the image, affect the performance of target recognition. Based on analyzing the ISAR echo model and typical motion compensation algorithm, the second chapter introduces the theory of compressed sensing sparse signal reconstruction method ISAR based on this method. The key from a statistical point of view presents a method for estimating sparse constraint parameters based on data. By deducing the reconstruction of high resolution map As the regularization problem, we obtain the closed form estimation by maximum likelihood parameter sparsity constraint. The noise variance from coarse image noise estimation unit is large, denoising image pre-processing by weight initialization. Using high resolution image optimization, then the parameters are updated about sparse beam and re estimation of the image, many times to raise the performance of the proposed algorithm. The results show that the sparse constraint parameter iterative estimation to high resolution in image reconstruction process, better balanced signal fidelity and sparsity. (2) coherent processing and high resolution imaging radar in addition to the work mode of diversification outside the sparse aperture, outside interference or system will defect data by forming a sparse aperture. For the block sparse sub aperture, the envelope alignment independent and self focusing treatment, residual linear phase and amplitude in complex observation aperture There are differences between. For the non coherence between the sparse aperture, the third chapter of this paper presents a coherent processing method. The envelope sub aperture according to centroid alignment after the establishment of the all pole model of each sub image in the display unit, and by the root MUSIC algorithm to estimate the pole, calculation of Doppler shift between the sub apertures in one of the sub aperture. As the benchmark calibration of linear phase, by least squares (Least Square LS) method for solving the model coefficients of each subaperture. By pole and coefficient estimation, frequency offset will be calibrated separately to the sub aperture before and after outwards to the whole aperture length. Then using LS to estimate complex the amplitude deviation is corrected. In coherent processing, construction part of FFT base as the observation matrix, the missing aperture can be restored by method of sparse signal processing. Can be seen from the experimental results, the coherent processing and Vacant aperture reconstruction imaging effect is improved obviously. (3) the uniform rotation target calibration method for target recognition in addition to the high resolution image, but also need to target size parameters accurately. Therefore, the fourth chapter and the fifth chapter according to the uniform rotation target presents two different calibration methods. The fourth chapter the first analysis of the potential impact of different calibration methods of residual translational phase motion compensation process introduced. Put forward an effective rotation speed using scattering point frequency cancellation (Effective Rotaional, Velocity, ERV) estimation method, this method is almost not affected by the residual translation. By selecting scatter point unit, and the the time domain signal as a time-varying autoregressive model. To estimate the short-term data segment instantaneous pole, Doppler won the whole course of sliding window aperture. According to the relationship, ERV from different distance unit Scattering point adjustable frequency calculation. The rotational phase compensation, then the autofocus processing to improve image focusing degree. Because of the scattering method depends on the quality of extracting, the signal-to-noise ratio (Signal-to-noise ratio SNR) low estimation performance will decrease. In order to improve the stability of the calibration algorithm, the fifth chapter for the uniform the overall performance of image rotation target ERV estimation method based on this method. Considering the influence of residual motion, the parameters of joint ERV estimation. Through iterative compensation two phase error, until after the compensation of the image sharpening square amplitude (Intensity-squared Sharpness, ISS) reached the maximum value at the same time, to obtain a focused image and ERV estimation. On this basis, the focused image autofocus processing, further focus image. Among them, ISS maximization problem is a typical nonlinear least squares problem, using Gauss Newton's method can To achieve efficient solution. Compared with other methods. The results show that the performance of the image from the angle estimation method of ERV although the efficiency of medium, but has high precision and robustness. (4) calibration of non uniform rotation of targets and high resolution imaging method with uniform acceleration caused by rotation of the distance and azimuth of two-dimensional coupled, increased the rotation parameter estimation difficult. For this goal, the sixth chapter puts forward a matching based on Fourier transform (Matched Fourier Transform, MFT) the calibration method of ISS image maximum. This method combines MFT transform RD imaging FFT linear transformation to replace parameters. Based on two-dimensional coupled iterative phase compensation the MFT image, the maximum ISS, the estimated MFT modulation frequency and ERV parameters. The calibration method can maintain a good estimation performance in the low SNR condition. But the strong noise or low resolution still affect the target Therefore, the reconstruction of high resolution images is further completed by using the method of partial MFT based and sparse signal processing.

【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TN957.52

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