發(fā)布時(shí)間：2018-07-06 08:44

  本文選題：陣列信號(hào)處理 + 信源數(shù)估計(jì)��； 參考：《吉林大學(xué)》2016年博士論文

【摘要】：陣列信號(hào)參數(shù)估計(jì)與跟蹤是陣列信號(hào)處理領(lǐng)域中一項(xiàng)重要的研究?jī)?nèi)容,其廣泛應(yīng)用于雷達(dá)探測(cè)、聲納定位、無(wú)線通信、地震勘探、射電天文以及生物醫(yī)學(xué)工程等眾多軍事與民用領(lǐng)域。陣列信號(hào)參數(shù)估計(jì)與跟蹤包括“參數(shù)估計(jì)”和“參數(shù)跟蹤”兩部分,其中陣列信號(hào)參數(shù)估計(jì)歷經(jīng)近幾十年的發(fā)展,其基本理論框架和基本算法已經(jīng)基本成熟,但隨著陣列信號(hào)參數(shù)估計(jì)從理論方法到實(shí)際工程應(yīng)用技術(shù)的轉(zhuǎn)變,人們對(duì)算法的穩(wěn)健性、估計(jì)精度以及計(jì)算復(fù)雜度等提出了更高的要求;而陣列信號(hào)參數(shù)跟蹤更是面臨著許多尚未得到很好解決的問(wèn)題。本文深入研究了其中的一些關(guān)鍵問(wèn)題,主要包括復(fù)雜噪聲背景下的信源數(shù)估計(jì)、相干信號(hào)的波達(dá)方向(DOA)估計(jì)、相干/同向信號(hào)的DOA與多普勒頻率聯(lián)合估計(jì)、DOA跟蹤以及多普勒頻率跟蹤等,針對(duì)以上問(wèn)題提出了一系列有效的算法,并取得了一些有意義的成果。本文的研究工作主要包括以下6個(gè)部分:第一,針對(duì)復(fù)雜噪聲背景下信源數(shù)估計(jì)問(wèn)題,提出了一種基于偽協(xié)方差矩陣的Otsu類(lèi)間方差法。由陣列輸出信號(hào)延時(shí)相關(guān)構(gòu)成的偽協(xié)方差矩陣對(duì)一定條件下的高斯白噪聲和高斯色噪聲具有免疫特性,并且利用陣元間的時(shí)間相關(guān)提高了陣列的有效孔徑,進(jìn)而提高了偽協(xié)方差矩陣奇異值分解后信號(hào)奇異值與噪聲奇異值的大小差異程度。最后利用Otsu類(lèi)間方差法對(duì)信號(hào)奇異值與噪聲奇異值進(jìn)行分類(lèi),進(jìn)一步提高了信源數(shù)的檢測(cè)成功概率。第二,針對(duì)相干信號(hào)DOA估計(jì)問(wèn)題,提出了一種基于特征空間MUSIC算法的空間平滑估計(jì)方法。首先對(duì)相干信號(hào)進(jìn)行空間平滑預(yù)處理,然后對(duì)其應(yīng)用特征空間MUSIC算法進(jìn)行DOA的有效估計(jì),使其最大限度地利用信號(hào)子空間和噪聲子空間的信息。所提方法并不影響非相干信號(hào)存在時(shí)DOA的估計(jì),且還可以對(duì)信號(hào)源功率進(jìn)行有效的估計(jì)。與傳統(tǒng)空間平滑算法及修正MUSIC算法相比,所提方法具有更低的信噪比門(mén)限和更高的估計(jì)精度及分辨力。第三,針對(duì)DOA與多普勒頻率聯(lián)合估計(jì)問(wèn)題,提出了兩種算法:1)針對(duì)DOA與多普勒頻率聯(lián)合估計(jì)的二維MUSIC算法在低信噪比、小快拍數(shù)等非理想條件下估計(jì)性能變差的問(wèn)題,提出了一種利用信號(hào)子空間投影方法修正的二維MUSIC算法。給出了一種運(yùn)用信號(hào)功率倒數(shù)加權(quán)的信號(hào)子空間投影方法,并將其與二維MUSIC算法進(jìn)行空間譜合成,充分利用信號(hào)子空間與噪聲子空間兩者的信息,提高了新算法在非理想條件下對(duì)多目標(biāo)DOA及多普勒頻率聯(lián)合估計(jì)的分辨性能。2)針對(duì)相干/同向信號(hào)DOA與多普勒頻率的聯(lián)合估計(jì)問(wèn)題,提出了一種基于數(shù)據(jù)共軛重構(gòu)的修正二維MUSIC算法。首先,建立包含DOA與多普勒頻率信息的廣義陣列信號(hào)模型,并對(duì)陣列協(xié)方差矩陣?yán)�用�?shù)據(jù)共軛重排加以重構(gòu),使其可有效適用于相干/同向信號(hào)下DOA與多普勒頻率的聯(lián)合估計(jì);其次,在二維MUSIC算法重構(gòu)的基礎(chǔ)上,利用1)中所提出的信號(hào)子空間投影方法對(duì)其加以修正,可以進(jìn)一步提高算法的分辨性能。所提算法還可以對(duì)信號(hào)的功率進(jìn)行有效估計(jì),且估計(jì)參數(shù)均可自動(dòng)配對(duì)。第四,針對(duì)最大似然估計(jì)方法計(jì)算量大的問(wèn)題,提出了一種基于序列二次規(guī)劃(SQP)的最大似然DOA及其與多普勒頻率聯(lián)合估計(jì)的方法。最大似然方法是一種在已知白噪聲情況下的貝葉斯最優(yōu)估計(jì)方法,在DOA、多普勒頻率等參數(shù)估計(jì)問(wèn)題中具有比特征子空間算法好得多的性能,并且它還可以直接處理相干信號(hào)。然而在其計(jì)算過(guò)程中需要非線性多維優(yōu)化求解,針對(duì)傳統(tǒng)網(wǎng)格搜索方法計(jì)算量過(guò)大的問(wèn)題,提出了一種全局最優(yōu)且局部具有超線性收斂特性的SQP方法,并把其應(yīng)用于最大似然的優(yōu)化求解中。最后通過(guò)仿真實(shí)驗(yàn)對(duì)其進(jìn)行了有效性驗(yàn)證。第五,針對(duì)運(yùn)動(dòng)目標(biāo)DOA跟蹤問(wèn)題,提出了一種時(shí)變遺忘因子的自適應(yīng)樣本協(xié)方差矩陣更新方法。時(shí)變遺忘因子根據(jù)DOA變化的快慢自適應(yīng)調(diào)節(jié)自身的大小,從而合理地調(diào)整歷史數(shù)據(jù)及當(dāng)前采樣數(shù)據(jù)在協(xié)方差矩陣更新過(guò)程中所占的權(quán)重;在協(xié)方差矩陣更新后,為了避免不斷重復(fù)地進(jìn)行特征值分解或奇異值分解,并且為了可以處理相干信號(hào),對(duì)更新的協(xié)方差矩陣直接應(yīng)用最大似然方法進(jìn)行DOA估計(jì)。同時(shí)針對(duì)最大似然方法計(jì)算量較大的問(wèn)題,分別利用人工蜂群仿生智能算法和SQP方法對(duì)其進(jìn)行優(yōu)化求解,有效減少了算法的運(yùn)算量,加快了算法的優(yōu)化速度,保證了跟蹤的實(shí)時(shí)性。第六,針對(duì)雷達(dá)信號(hào)多普勒頻率跟蹤問(wèn)題,提出了一種基于動(dòng)態(tài)壓縮感知的跟蹤估計(jì)算法。首先建立雷達(dá)信號(hào)的稀疏時(shí)變信號(hào)模型,然后根據(jù)上一測(cè)量時(shí)刻稀疏向量中提取出的先驗(yàn)稀疏位置信息,構(gòu)建當(dāng)前時(shí)刻的冗余字典,并同時(shí)獲得當(dāng)前測(cè)量時(shí)刻稀疏向量中非零元素的分布概率,建立起多普勒頻率的稀疏概率模型。最后,通過(guò)求解一個(gè)加權(quán)的l1范數(shù)最小化問(wèn)題對(duì)當(dāng)前稀疏信號(hào)進(jìn)行重構(gòu),獲得其非零元素位置,從而實(shí)現(xiàn)對(duì)多普勒頻率的動(dòng)態(tài)實(shí)時(shí)跟蹤。仿真實(shí)驗(yàn)驗(yàn)證了所提算法的正確性與有效性。
[Abstract]:The estimation and tracking of array signal parameters is an important research content in the field of array signal processing. It is widely used in many military and civil fields, such as radar detection, sonar positioning, wireless communication, seismic exploration, radio astronomy and biomedical engineering. The estimation and tracking of array signal parameters include "parameter estimation" and "parameter". Following the development of the array signal parameter estimation in the past few decades, the basic theoretical framework and basic algorithms have been basically mature, but with the change of the estimation of the array signal parameters from the theoretical method to the actual engineering application technology, the robustness, the estimation accuracy and the computational complexity of the algorithm are higher than that of the two parts. A number of key problems are discussed in this paper, including the source number estimation under the complex noise background, the direction of arrival (DOA) estimation of coherent signals, the DOA of coherent / Homo signal and the Doppler frequency estimation, and the DOA tracking. And Doppler frequency tracking and so on, a series of effective algorithms are proposed and some meaningful results have been obtained. The research work of this paper mainly includes the following 6 parts: firstly, a Otsu inter class variance method based on pseudo covariance matrix is proposed for the source number estimation in complex noise background. The pseudo covariance matrix of the signal delay correlation is immune to the Gauss white noise and the Gauss color noise under certain conditions, and the effective aperture of the array is improved by the time correlation between the elements. The difference degree between the singular value of the pseudo covariance matrix and the singular value of the noise is improved. Then the Otsu inter class variance method is used to classify the singular value of signal and the singular value of noise, and the detection success probability of the number of sources is further improved. Second, in view of the DOA estimation problem of coherent signals, a spatial smoothing estimation method based on the feature space MUSIC algorithm is proposed. It uses the feature space MUSIC algorithm to estimate the DOA effectively, so that it can maximize the information of the signal subspace and the noise subspace. The proposed method does not affect the estimation of the DOA when the incoherent signal exists, and it can also effectively estimate the power of the signal source. Compared with the traditional spatial smoothing algorithm and the modified MUSIC algorithm, The proposed method has lower SNR threshold and higher estimation accuracy and resolution. Third, in view of the joint estimation of DOA and Doppler frequency, two algorithms are proposed: 1) the two dimensional MUSIC algorithm, which is combined with DOA and Doppler frequency estimation, is proposed to estimate the performance deterioration under low SNR, small snapshot number and other non ideal conditions. A two-dimensional MUSIC algorithm modified by the signal subspace projection method is presented. A signal subspace projection method using the weighted inverse of the signal power is given, and the spatial spectrum is synthesized with the two-dimensional MUSIC algorithm, and the information between the signal subspace and the noise subspace is fully utilized, and the new algorithm is improved in the non ideal condition. Target DOA and Doppler frequency joint estimation resolution.2) a modified two-dimensional MUSIC algorithm based on data conjugate reconstruction is proposed for the joint estimation of coherent / Homo signal DOA and Doppler frequency. First, a generalized array signal model containing DOA and Doppler frequency information is established, and the array covariance matrix is used. The data conjugate rearrangement is reconstructed so that it can be effectively applied to the joint estimation of the DOA and the Doppler frequency under the coherent / identical signal. Secondly, on the basis of the reconstruction of the two-dimensional MUSIC algorithm, the signal subspace projection method proposed in 1) can be used to improve the resolution of the algorithm. The power of the number is effectively estimated and the estimated parameters can be automatically paired. Fourth, a maximum likelihood DOA based on the sequence two times programming (SQP) and a joint estimation of the Doppler frequency are proposed for the maximum likelihood estimation. The maximum likelihood method is a kind of Bias with known white noise. The optimal estimation method has much better performance in the DOA, Doppler frequency and other parameter estimation problems, and it can also directly deal with the coherent signals. However, the nonlinear multidimensional optimization is needed in the calculation process, and a global optimum is proposed for the problem of too large computation in the traditional grid search method. The SQP method with superior and local superlinear convergence is applied to the optimal solution of maximum likelihood. Finally, the validity of the method is verified by simulation experiments. Fifth, an adaptive sample covariance matrix updating method for time-varying forgetting factor is proposed for the DOA tracking problem of moving target. According to the fast and slow change of the DOA, the weight of the historical data and the current sampling data in the covariance matrix update process is adjusted reasonably. After the covariance matrix is updated, in order to avoid repeated eigenvalue decomposition or singular value decomposition, and in order to process the coherent signal, the updated The covariance matrix uses the maximum likelihood method to estimate DOA directly. At the same time, aiming at the problem that the maximum likelihood method has large computational complexity, the artificial swarm intelligence algorithm and the SQP method are used to optimize the calculation, which can effectively reduce the computation of the algorithm, speed up the optimization speed of the algorithm, and ensure the real-time performance of the tracking. Sixth, the needle is guaranteed. For radar signal Doppler frequency tracking problem, a tracking estimation algorithm based on dynamic compression perception is proposed. Firstly, the sparse time-varying signal model of radar signal is set up, and then according to the prior sparse position information extracted from the sparse vector of the last measurement time, the redundant dictionary of the front time is constructed and the current measurement is obtained at the same time. The distribution probability of non zero element in the time sparse vector is established, and the sparse probability model of the Doppler frequency is established. Finally, by solving a weighted L1 norm minimization problem, the current sparse signal is reconstructed and its non zero element position is obtained, thus the dynamic real-time tracking of the Doppler frequency is realized. The simulation experiment verifies the proposed algorithm. Correctness and effectiveness.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TN911.23

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