本文選題：雷達(dá)目標(biāo)識(shí)別 + 特征提取　； 參考：《西安電子科技大學(xué)》2014年博士論文

【摘要】：雷達(dá)成像技術(shù)具有全天候、全天時(shí)、遠(yuǎn)距離觀測(cè)能力，有效提高了雷達(dá)的信息獲取能力，具有重要的軍用和民用應(yīng)用價(jià)值。隨著雷達(dá)成像技術(shù)的高速發(fā)展，雷達(dá)圖像收集能力越來(lái)越強(qiáng)。從大量雷達(dá)圖像中獲取目標(biāo)信息進(jìn)行目標(biāo)檢測(cè)、分類與識(shí)別則是雷達(dá)成像的目的，其中基于雷達(dá)圖像的目標(biāo)識(shí)別受到越來(lái)越廣泛的關(guān)注。傳統(tǒng)的基于數(shù)據(jù)驅(qū)動(dòng)的目標(biāo)識(shí)別方法依賴于數(shù)據(jù)本身所反映出來(lái)的目標(biāo)信息，而在實(shí)際環(huán)境中數(shù)據(jù)反映的目標(biāo)信息隨環(huán)境變化而變化，這給基于數(shù)據(jù)驅(qū)動(dòng)的目標(biāo)識(shí)別方法帶來(lái)挑戰(zhàn)。目標(biāo)的一些物理特征（如目標(biāo)幾何尺寸，物理結(jié)構(gòu)等）受環(huán)境因素影響較小，而且雷達(dá)成像機(jī)理在一定程度上可以反映目標(biāo)物理特征。本文以此為出發(fā)點(diǎn)，研究了基于目標(biāo)參數(shù)化回波模型雷達(dá)圖像中目標(biāo)物理特征提取技術(shù)。論文分別從干涉ISAR（InISAR）橫向定標(biāo)、目標(biāo)微動(dòng)特征提取以及目標(biāo)電磁特征（屬性散射中心特征與極化特征）三個(gè)方面探討和研究目標(biāo)物理特征提取技術(shù)，主要工作概括如下： 1.針對(duì)干涉ISAR（InISAR）橫向定標(biāo)中相位纏繞問題，提出一種基于隨機(jī)霍夫變換（Randomized Hough Transform, RHT）的InISAR橫向定標(biāo)算法。該算法利用ISAR圖像中的特顯點(diǎn)的模糊橫距（干涉相位主值得到的橫距）與多普勒頻率的線性關(guān)系，估計(jì)真實(shí)橫距與多普勒頻率之間的尺度因子，實(shí)現(xiàn)對(duì)目標(biāo)ISAR圖像橫向定標(biāo)，從而避免了繁瑣的相位解纏繞過程。仿真實(shí)驗(yàn)結(jié)果表明該算法可以實(shí)現(xiàn)對(duì)ISAR圖像的定標(biāo)，并具有一定的抗噪聲性能，由定標(biāo)后的目標(biāo)ISAR圖像可以提取目標(biāo)的幾何尺寸特征。 2.研究了目標(biāo)微動(dòng)特征提取方法，主要包含兩部分：（1）在分析微動(dòng)目標(biāo)窄帶回波信號(hào)基礎(chǔ)上，指出窄帶目標(biāo)的微動(dòng)特征提取等價(jià)于多分量非平穩(wěn)信號(hào)的瞬時(shí)頻率估計(jì)問題，我們提出一種基于曲線跟蹤（Curve track, CT）算法的目標(biāo)微動(dòng)特征提取方法。該算法在時(shí)頻域通過最近鄰數(shù)據(jù)關(guān)聯(lián)（Nearest Neighbor DataAssociation, NNDA）算法對(duì)各分量信號(hào)的時(shí)頻曲線進(jìn)行關(guān)聯(lián)與分離，然后采用擴(kuò)展卡爾曼（Kalman）濾波器對(duì)分離的時(shí)頻曲線進(jìn)行平滑濾波，并基于平滑后的時(shí)頻曲線估計(jì)目標(biāo)微動(dòng)參數(shù)。（2）通過分析微動(dòng)目標(biāo)的寬帶回波，指出目標(biāo)回波在距離包絡(luò)域具有微距特征，以及時(shí)頻域具有微多普勒特征。對(duì)于微距特征提取，利用幅度相位估計(jì)方法（Amplitude and phase estimation, APES）得到目標(biāo)距離包絡(luò)信號(hào)的超分辨估計(jì)，在此基礎(chǔ)上由CT算法實(shí)現(xiàn)目標(biāo)微距特征曲線的分離與提�。会槍�(duì)微多普勒特征提取，利用多距離單元信號(hào)進(jìn)行聯(lián)合時(shí)頻分析得到目標(biāo)完整的多普勒譜，并由CT算法提取目標(biāo)微多普勒特征。最后基于電磁計(jì)算數(shù)據(jù)的仿真結(jié)果驗(yàn)證所提算法的有效性。 3.研究了屬性散射中心提取方法，包含兩部分：（1）考慮目標(biāo)頻率-方位二維觀測(cè)數(shù)據(jù)在屬性散射中心模型參數(shù)空間上的稀疏性，，我們提出一種基于字典縮放的屬性散射中心提取與參數(shù)估計(jì)方法。由于模型參數(shù)維數(shù)較高，構(gòu)造的高維聯(lián)合字典將消耗較多系統(tǒng)資源。針對(duì)該問題，所提算法采用交替優(yōu)化與字典縮放實(shí)現(xiàn)了對(duì)參數(shù)化字典的降維。為了減小鄰近散射中心之間的相互干擾，采用正交匹配追蹤（OMP）-RELAX聯(lián)合算法求解稀疏信號(hào)恢復(fù)問題，實(shí)現(xiàn)在頻率-方位角域提取屬性散射中心。（2）我們提出一種基于距離特性與方位特性解耦合的屬性散射中心提取算法，進(jìn)一步降低對(duì)系統(tǒng)資源的要求。該方法通過分別構(gòu)建包含位置信息與方位特性信息的兩個(gè)低維字典代替高維的聯(lián)合字典實(shí)現(xiàn)距離特性與方位特性的解耦合，并得到散射中心參數(shù)估值。根據(jù)提取的屬性散射中心可以有效地估計(jì)目標(biāo)或目標(biāo)重要部件的幾何尺寸�；陔姶庞�(jì)算數(shù)據(jù)和實(shí)測(cè)數(shù)據(jù)的實(shí)驗(yàn)結(jié)果驗(yàn)證了上述算法的有效性。 4.研究了全極化屬性散射中心提取方法：（1）考慮目標(biāo)全極化觀測(cè)數(shù)據(jù)在屬性散射中心模型參數(shù)空間上的聯(lián)合稀疏特性，利用聯(lián)合稀疏表示技術(shù)提取屬性散射中心，并對(duì)估計(jì)的極化散射矩陣進(jìn)行極化分解提取目標(biāo)極化特征，聯(lián)合干涉測(cè)高可以得到目標(biāo)三維姿態(tài)信息。該方法采用基于字典縮放屬性散射中心提取算法思想實(shí)現(xiàn)參數(shù)化字典的降維，對(duì)稀疏系數(shù)矩陣施加行稀疏約束，通過SOMP（Simultaneous Orthogonal Matching Pursuit）算法求解聯(lián)合稀疏優(yōu)化問題并提取屬性散射中心。（2）針對(duì)散射中心重疊或者同一分辨單元內(nèi)包含不止一種散射體的情況，依據(jù)目標(biāo)全極化觀測(cè)在屬性特征域（屬性參數(shù)以及散射類型）的稀疏特性，對(duì)目標(biāo)極化分解系數(shù)矩陣施加行稀疏約束與矩陣稀疏約束，該算法利用坐標(biāo)輪回下降法估計(jì)目標(biāo)極化分解系數(shù)矩陣與極化散射機(jī)理字典，同時(shí)提取目標(biāo)全極化屬性散射中心及其極化特征。基于電磁計(jì)算數(shù)據(jù)的實(shí)驗(yàn)結(jié)果驗(yàn)證了上述算法的有效性。
[Abstract]:Radar imaging technology has all weather, all day time, long distance observation ability, effectively improve the radar information acquisition ability, has important military and civil application value. With the rapid development of radar imaging technology, radar image collection ability is more and more strong. Target information acquisition from a large number of radar images for target detection, classification and Recognition is the purpose of radar imaging, in which the target recognition based on radar image is becoming more and more widely concerned. The traditional data driven target recognition method relies on the target information reflected by the data itself, and the target information reflected in the actual environment changes with the environment, which is based on the data drive. The moving target recognition method brings challenges. Some physical features of the target (such as the target geometry, physical structure, etc.) are less affected by the environmental factors, and the radar imaging mechanism can reflect the physical characteristics of the target to a certain extent. This paper takes this as the starting point, and studies the target physics based on the target parameterized echo model radar image. Feature extraction technology. This paper discusses and studies the target physical feature extraction technology from three aspects: interference ISAR (InISAR) lateral calibration, target microdynamic feature extraction and target electromagnetic characteristics (attribute scattering center characteristics and polarization characteristics). The main work is summarized as follows:
1. for the phase winding problem in the interference ISAR (InISAR) lateral calibration, a InISAR lateral calibration algorithm based on random Hof transform (Randomized Hough Transform, RHT) is proposed. The algorithm uses the linear relationship between the blurred transverse distance of the explicit point of the ISAR image and the Doppler frequency, and estimates the true transverse distance. The scale factor between the Doppler frequency and the Doppler frequency can realize the horizontal calibration of the target image, thus avoiding the tedious phase winding process. The simulation results show that the algorithm can achieve the calibration of the ISAR image, and has certain anti noise performance. The target ISAR image can extract the geometric feature of the target by the target ISAR image.
2. the extraction method of target micromotion features is studied. It mainly includes two parts: (1) on the basis of the analysis of the narrow band echo signal of the moving target, it points out that the micro motion feature of the narrowband target is extracted from the instantaneous frequency estimation equivalent to the multicomponent nonstationary signal, and we propose a target fretting feature based on the Curve track (CT) algorithm. The algorithm uses the nearest neighbor data association (Nearest Neighbor DataAssociation, NNDA) algorithm to correlate and separate the time-frequency curves of each component signal in time and frequency domain, and then uses an extended Calman (Kalman) filter to smooth the separated time frequency curves and estimate the target micro based on the smooth time frequency curve. (2) by analyzing the wide-band echo of the moving target, it is pointed out that the target echo has a microdistance feature in the range envelope domain and the time frequency domain has the characteristics of micro Doppler. For the extraction of the microdistance feature, the range phase estimation method (Amplitude and phase estimation, APES) is used to obtain the super-resolution estimation of the target distance envelope signal. The CT algorithm is used to separate and extract the target micro feature curve. According to the feature extraction of micro Doppler, the integrated time frequency analysis is used to obtain the complete target Doppler spectrum by the multi distance unit signal, and the target micro Doppler feature is extracted by the CT algorithm. Finally, the simulation results of the electromagnetic calculation data verify the proposed algorithm. Efficiency.
3. the extraction method of attribute scattering center is studied, including two parts: (1) considering the sparsity of the target frequency azimuth observation data in the parameter space of the attribute scattering center model, we propose a method of extracting and estimating the attribute scattering center based on the dictionary scaling. The high dimension joint of the model is constructed because of the high dimension of the model parameter. The proposed algorithm uses alternate optimization and dictionary scaling to reduce the dimension of the parameterized dictionary. In order to reduce the interference between the adjacent scattering centers, the orthogonal matching tracking (OMP) -RELAX algorithm is used to solve the sparse signal recovery problem, and the frequency azimuthal domain extraction is realized. (2) we propose an attribute scattering center extraction algorithm based on the decoupling of distance and azimuth characteristics, which can further reduce the demand for system resources. By constructing two low dimensional dictionaries, which include location information and azimuth characteristics, the distance characteristics and orientation are replaced by high dimensional joint dictionaries. The parameter estimation of the scattering center is obtained. The geometric size of the target or the important part of the target can be estimated effectively according to the extracted attribute scattering center. The experimental results based on the electromagnetic calculation data and the measured data verify the effectiveness of the proposed algorithm.
4. (1) considering the joint sparse characteristic of the target fully polarized observation data in the parameter space of the attribute scattering center model, the joint sparse representation technique is used to extract the attribute scattering center, and the polarization decomposition of the estimated polarization scattering matrix is used to extract the polarization characteristics of the target, and the joint interferometry is used. In this method, the three-dimensional attitude information of the target can be obtained. This method uses the idea of the dictionary scaling attribute scattering center extraction algorithm to reduce the dimension of the parameterized dictionary, applies the sparse constraint on the sparse coefficient matrix, and solves the joint sparse optimization problem by SOMP (Simultaneous Orthogonal Matching Pursuit) algorithm and extracts the attribute scattering center. (2) in view of the overlapping of the scattering centers or the case of more than one scatterer in the same resolution unit, based on the sparse characteristic of the target fully polarized observation in the attribute domain (attribute parameters and scattering types), the line sparse constraint and the sparse constraint are applied to the polarization decomposition coefficient matrix of the target, and the algorithm is estimated by the coordinate rotation descent method. The target polarization decomposition coefficient matrix and the polarization scattering mechanism dictionary are used to extract the full polarization attribute scattering center of the target and its polarization characteristics. The experimental results based on the electromagnetic calculation data verify the effectiveness of the proposed algorithm.
【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】：2014
【分類號(hào)】：TN957.52

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