基于压缩感知的稀疏无源雷达成像

当窄带外辐射源数目稀少且空间分布不均匀时,通常会在无源雷达成像中产生稀疏的无规则空间谱填充,使得传统快速逆傅里叶方法(inverse fast Fourier transform,IFFT)或极坐标方法难以获得良好的目标成像效果。针对这种空间谱填充的稀疏性和非均匀性,利用压缩感知理论在处理稀疏随机采样信号重构问题上的优势,提出了稀疏无源雷达成像方法。同时通过构造传感矩阵的互相关和积累相关函数,对目标图像的可重构性进行了分析。理论分析和仿真结果表明,对具有稀疏随机空间谱特点的无源雷达成像,本文提出的成像方法是有效的。

稀疏点源波束干涉合成建模与特征提取算法

多点源情形下,稀疏阵列波束通过干涉合成后的能量分布受到多种复杂因素影响,难以对交汇区域内功率分布规律进行简单的理论分析。针对这一问题,提出了稀疏点源波束干涉合成模型,阐述了有效功率点与有效功率区的概念。并深入分析有效功率区的特征,借鉴数学形态学的图像处理技术,设计了一套特征提取算法,对有效功率区个数、质心位置、面积、有效点平均间隔等特征参数进行了量化提取。仿真实验表明,该方法可以为分析与评估稀疏阵波束功率合成效果提供解决途径和理论依据。

基于CMMB信号的稀疏无源探测算法

分析了基于外辐射源信号的目标探测原理,从信号频率、带宽、调制方式、覆盖范围、功率和独有特性等角度对中国移动多媒体广播(CMMB)信号进行了研究,理论推导了CMMB信号的模糊函数等相关特性的数学模型。针对CMMB信号特点,提出了稀疏无源探测算法。仿真与分析表明,该算法可以抑制模糊函数多普勒频移维和时延维的旁瓣,同时不再需要对回波信号中直达波信号进行提纯,具有较大应用潜力。

稀疏数据源频繁模式挖掘并行算法

针对频繁模式挖掘中一类特殊的稀疏数据源,设计了一种链表结构体FI-list,并据此提出一个并行搜索频繁项集(PMFSD)的算法.该方法基于一个分布式共享内存系统——面向视图的分布式集群计算(VODCA)而设计.详细描述了链表结构体FI-list的设计和构建过程,论述了在VODCA上挖掘稀疏数据源频繁模式的问题中,视图的划分依据及划分结果,讨论了算法实现的动态任务分配策略.实验结果证明了PMFSD算法的正确性和有效性.

基于稀疏表征的欠定盲源分离的研究

稀疏信号处理是研究欠定盲源分离的主要手段。本文介绍了稀疏分量分析的基本模型,在此基础上深入分析并讨论了基于稀疏表示的盲源分离算法。该算法对实际欠定混叠盲信号分离有重要的意义。

基于霍夫直线检测的稀疏盲分离

提出了基于霍夫变换直线检测的稀疏盲分离算法,直接利用信号数据,提取其在空间分布的直线方向,消除了初始化的随机性,降低了对信号稀疏度的要求,精确地估计出了混合矩阵,从而达到分离出源信号的目的。给出仿真结果,验证了算法的有效性。

多点源交叉波束干涉效应特征分析

介绍多点源交叉波束通过干涉效应实现交汇空间功率合成的基本原理,根据空间功率合成机理,建立对功率合成效率、同相叠加点间距的计算机仿真,完成多点源交叉波束干涉效应特征分析,以此作为多点源交叉波束空间功率合成的评估依据。

信源数量估计的可视化线性聚类方法

在通过传感器采集信源获得观测数据的过程中,估计信源的数量对源信号处理和观测数据分析起着非常重要的作用。为了确定稀疏信源的数量,本文提出了增强信号线性聚类特性的可视化估计方法。首先,利用短时傅里叶变换(STFT)把时域的观测信号变换成频域中的复频谱以增强观测数据的稀疏性;然后,建立一种角度余弦的相似性测度,以频谱实部分量与虚部分量之间的角度阈值来判别数据点所归属的信源;最后,把该角度阈值应用于单源点(SSP)检测中,剔除造成干扰的多源点(MSP)数据,凸显稀疏信源的线性聚类特性。实验结果表明,本文方法可以有效地增强数据的线性聚类特性,实现对信源数量直观地估计。

欠定条件下的盲分离算法

盲信号分离中当源信号个数大于观测信号个数,且源信号不是足够稀疏时,如果利用聚类算法进行分离,分离效果将会变差。为此提出一种在此欠定条件下新的盲信号分离算法。利用源信号的“稀疏性”估计混合矩阵,然后简化混合矩阵构造新的混合模型。由于源信号间具有的独立性,使得可以在新的混合模型中从观察信号的自相关函数中估计出源信号的频谱,从而达到分离出源信号的目的,且分离效果优于聚类算法。最后给出仿真试验实例,试验结果验证了算法的有效性。

Underdetermined Blind Source Separation of Adjacent Satellite Interference Based on Sparseness

The problem of underdetermined blind source separation of adjacent satellite interference is proposed in this paper. Density Clustering algorithm(DC-algorithm) presented in this article is different from traditional methods. Sparseness representation has been applied in underdetermined blind signal source separation. However, some difficulties have not been considered, such as the number of sources is unknown or the mixed matrix is ill-conditioned. In order to find out the number of the mixed signals, Short Time Fourier Transform(STFT) is employed to segment received mixtures. Then, we formulate the blind source signal as cluster problem. Furthermore, we construct Cost Function Pair and Decision Coordinate System by using density clustering. At the end of this paper, we discuss the performance of the proposed method and verify the novel method based on several simulations. We verify the proposed method on numerical experiments with real signal transmission, which demonstrates the validity of the proposed method.

Sparse constrained encoding multi-source full waveform inversion method based on K-SVD dictionary learning

Full waveform inversion(FWI)is an extremely important velocity-model-building method.However,it involves a large amount of calculation,which hindsers its practical application.The multi-source technology can reduce the number of forward modeling shots during the inversion process,thereby improving the efficiency.However,it introduces crossnoise problems.In this paper,we propose a sparse constrained encoding multi-source FWI method based on K-SVD dictionary learning.The phase encoding technology is introduced to reduce crosstalk noise,whereas the K-SVD dictionary learning method is used to obtain the basis of the transformation according to the characteristics of the inversion results.The multiscale inversion method is adopted to further enhance the stability of FWI.Finally,the synthetic subsag model and the Marmousi model are set to test the effectiveness of the newly proposed method.Analysis of the results suggest the following:(1)The new method can effectively reduce the computational complexity of FWI while ensuring inversion accuracy and stability;(2)The proposed method can be combined with the time-domain multi-scale FWI strategy flexibly to further avoid the local minimum and to improve the stability of inversion,which is of significant importance for the inversion of the complex model.

Energy-constrained ferry route design for sparse wireless sensor networks

In recent years,using message ferries as mechanical carriers of data has been shown to be an effective way to collect information in sparse wireless sensor networks.As the sensors are far away from each other in such highly partitioned scenario,a message ferry needs to travel a long route to access all the sensors and carry the data collected from the sensors to the sink.Typically,practical constraints(e.g.,the energy)preclude a ferry from visiting all sensors in a single tour.In such case,the ferry can only access part of the sensors in each tour and move back to the sink to get the energy refilled.So,the energy-constrained ferry route design(ECFRD)problem is discussed,which leads to the optimization problem of minimizing the total route length of the ferry,while keeping the route length of each tour below a given constraint.The ECFRD problem is proved to be NP-hard problem,and the integer linear programming(ILP)formulation is given.After that,efficient heuristic algorithms are proposed to solve this problem.The experimental results show that the performances of the proposed algorithms are effective in practice compared to the optimal solution.

Source number estimation and separation algorithms of underdetermined blind separation

Recently,sparse component analysis (SCA) has become a hot spot in BSS re-search. Instead of independent component analysis (ICA),SCA can be used to solve underdetermined mixture efficiently. Two-step approach (TSA) is one of the typical methods to solve SCA based BSS problems. It estimates the mixing matrix before the separation of the sources. K-means clustering is often used to estimate the mixing matrix. It relies on the prior knowledge of the source number strongly. However,the estimation of the source number is an obstacle. In this paper,a fuzzy clustering method is proposed to estimate the source number and mixing matrix simultaneously. After that,the sources are recovered by the shortest path method (SPM). Simulations show the availability and robustness of the proposed method.

Rapid earthquake focal mechanism inversion using high-rate GPS velometers in sparse network

In this study, we demonstrate an approach for inverting earthquake source parameters based on high-rate global positioning system （GPS） velocity seismograms. The velocity records obtained from single-station GPS velocity solutions with broadcast ephemeris are used directly for earthquake source parameter inversion using the Cut and Paste method, without requiring conversion of the velocity records into displacement records. Taking the E1 Mayor-Cucapah earthquake as an example, GPS velocity records from 10 stations with reasonable azimuthal coverage provide earthquake source parameters very close to those from the Global centroid moment tensor （Global CMT） solution. In sparse network tests, robust source parameters with acceptable bias can be achieved with as few as three stations. When the number of stations is reduced to two, the bias in rake angle becomes appreciable, but the magnitude and strike estimations are still robust. The results of this study demonstrate that rapid and reliable estimation of earthquake source parameters can be obtained from GPS velocity data. These parameters could be used for early earthquake warning and shake map construction, because such GPS velocity records can be obtained in real time.