【作者】 刘娟娟；

【导师】 孙晓颖；

【作者基本信息】 吉林大学 ， 通信与信息系统， 2013， 硕士

【摘要】 近场源定位是阵列信号处理的一个重要研究方向，广泛应用于无线通信、雷达、声纳、震源勘测等领域，主要研究利用阵列接收的数据如何确定信源方向，即确定估计信源的到达角及距离。近年来，针对近场源定位研究，已经取得了优秀的成果，但是对来波信号本身的时频信息利用并不充分，且在相干源处理能力、计算量等方面通常存在局限。将定位技术转化为稀疏重构问题为这些问题的解决带来了新的解决途径。本文以稀疏分解理论为主要数学工具，结合时频分析，对近场复杂源定位问题进行了深入研究。针对传统的近场源定位方法不能直接用于处理相干源，本文将稀疏分解引入近场源定位中提出稀疏定位方法，为了避免算法的高计算量，选择了贪婪算法作为重构方法。计算机仿真实验表明，稀疏定位方法不仅具有较低的计算量，对噪声具有鲁棒性，并能有效处理相干源。为了提高基于贪婪算法的稀疏定位方法的估计精度，本文重点研究了基于旋转阵列的稀疏定位方法。联合利用不同指向的阵列相当于增加了接收传感器及采样点数，从而提供了高的估计精度。为了减少构造的稀疏基原子之间相干性对稀疏定位算法分辨率的影响，提出了研究了相干抑制IHT算法的近场源定位方法，使选择出的原子满足最小的相干性要求，从而提供了准确的估计。当信源空间位置很近时，利用信源等能量的性质提出了修正算法，使定位算法具有较高的分辨率。仿真实验结果表明，这两种改进的稀疏定位算法提供了高的估计精度、分辨率，同时能有效处理相干源。针对非平稳信源定位问题，结合信号时频域的稀疏性及信源空间位置稀疏性综合研究了非平稳信源的稀疏定位方法。利用非平稳信号在时频域有规律的特点，建立了时频稀疏定位模型，并利用贪婪算法求解稀疏问题得到信源位置。当信源为未知非平稳信源时，需要估计信号的调频参数，为此研究了利用信号在时频域的稀疏性来重构并估计其调频参数估计算法。时频稀疏定位算法的具有超高的估计精度、分辨率，并且估计的信源数不受阵元数的限制。针对未知非平稳信号，首先研究结合MP算法及Hough变换的信号检测方法，仿真表明该方法提供了在信噪比很低的情况下仍然提供了较高的估计准确度率，但是Gabor原子库中巨大的原子数及需要求WVD导致算法计算量偏大。因此进一步研究改进的调频参数估计方法，该方法固定了时频点以减少原子库中原子数，避免求WVD，而只记录原子所在的位置，并利用Hough变换来检测LFM信号估计调频参数。通过计算机仿真分析了两种调频参数估计方法的优缺点，并将其用于时频稀疏定位算法中证明了本文主要创新工作如下：(1)建立了基于空间稀疏的近场源定位模型，提出了基于贪婪算法的近场源定位算法，仿真表明稀疏定位算法提供了适中的计算量、较高的估计精度、分辨率，并可以处理相干源等优点。(2)提出了基于传感器阵列的非平稳信号调频参数估计方法，该方法利用非平稳信号在时频域的稀疏性，结合贪婪算法及Hough变换检测信号，仿真表明算法提供了较高的准确率，能被有效用于信源定位中，显著提高了定位算法性能，为定位性能的提高做出一定的贡献。(3)推导了稀疏时频定位模型，基于模型提出了稀疏时频定位方法，由于可以分别估计信源位置，因此该方法提供了较高的估计精度及分辨率，同时突破了信源数必须少于阵元数的限制。本文提出的近场源定位算法可以应用于任意非相干或相干情形，具有广泛的实用前景。同时本文提出的基于传感器阵列的非平稳信源检测算法具有良好的性能，对信源检测研究具有一定的参考价值。更多还原

【Abstract】 As an important problem in array signal processing, near-field sourcelocalization studies how to obtain the source localization from the received data ofarray, which contains the direction of arrival and range parameters. Also near-fieldsource localization is widely applied in radar, sonar, speech enhancement, electronicsurveillance and seismic exploration etc. In recent years, aiming at the near-fieldlocalization problem, many excellent achievements have been achieved. But they donot sufficiently exploit the signals’ time-frequency domain information. And theyusually have some drawbacks in coherent signal processing and high accuracy withhigh computational complexity. We consider the source localization problem based onsensor array as a sparse representation problem, which brings a new ways to solveabove problems for localization method. So in this dissertation, combinedtime-frequency analysis with sparse representation, we research the localizationproblem of near-field sources.To overcome the weakness that traditional methods can not deal with thecoherent sources directly, the dissertation incorporates the sparse representation intothe localization methods to propose sparse localization methods. In order to avoidheavy computational load, the greedy algorithms are chosen as the reconstructionalgorithm. We conduct extensive numerical experiments analyzing the behavior ofour approach and comparing it to existing source localization methods. This analysisdemonstrates that our approach has important advantages such as low computationalcomplex, robustness to noise and robustness to coherent sources.In order to improve the accuracy of above sparse localization algorithm, thedissertation mainly studies a sparse localization method based on dynamic array,which jointly exploits the received data of different orientation array, that equal toadding the sensor and sample number. To reduce the influence of atoms in sparse baseon the resolution for localization method, the iterative hard thresholding(IHT) basedon coherence-inhibiting is studied, which prunes some support atoms to leave the lowcorrelation support atoms for the sparse reconstruction. A modified method isproposed for the situation where false DOAs are obtained due to the closely space of sources. The modified method is based on the approximately equal energyinformation of sources. The simulation results show that the improved methodsprovide improved resolution and high estimation accuracy in the low SNR, and canefficiently localize the coherent sources.To solve the localization problem, combining the time-frequency distributionswith sparse representation, we study localization algorithm of near-field sources, thetime-frequency distribution of unstationary signal has regular characteristic, whichcan be incorporated into localization method to establish the time-frequency sparselocalization model, which can be solved by greedy algorithm. The proposedtime-frequency localization method provide super resolution and high accuracy, andno requirement about the less source number than sensors.If the incoming sources are unknown unstationary signals, we firstly propose thesignal detection method based on MP algorithm and Hough transform. The simulationresults show that the method provide high correct probability even at low SNR. But itsuffers heavy computation burden due to the number of atoms in Garbor dictionary,so we propose to construct the dictionary with fixed time, and instead the WVD bythe localization of selected atoms. And then the Hough transform is used to detect theFM parameters. Simulation demonstrates the advantages and disadvantages of twodetection algorithms.The main work can be summarized as follows:(1) Based on the spatial sparsity, the sparse localization model for near-fieldsource was built, and proposed near-field localization method using greedy algorithm.The simulation results show that sparse localization algorithm provide moderatecalculation load, high estimation accuracy and resolution, at same time, it can dealwith coherent sources.(2) Using the sparsity of nostationary signal in time-frequency domain, themethods for detecting FM parameters based on received array are proposed, whichcombined greedy algorithm and Hough transform. The simulation results show thatdetection methods provide high correct probability. So they can be efficiently appliedto localization algorithm, which made some contributions to enhance the performanceof the algorithm.(3) The sparse time-frequency localization model is derived, and proposed thesparse time-frequency localization method. Due to the localization of sources can be estimated separately, the method provided high estimation accuracy and resolution,and no requirement about the less source number than sensors.The near-field localization algorithms presented in this paper can be applied toany sources that the source signals are correlated or uncorrelated, which has broadapplication prospects. The detection algorithm based using array for nonstationarysources have good performance, which has a certain reference value.更多还原