DOA estimation in unknown colored noise using covariance differencing and sparse signal recovery

A direction-of-arrival （DOA） estimation algorithm is presented based on covariance differencing and sparse signal recovery, in which the desired signal is embedded in noise with unknown covariance. The key point of the algorithm is to eliminate the noise component by forming the difference of original and transformed covariance matrix, as well as cast the DOA estimation considered as a sparse signal recovery problem. Concerning accuracy and complexity of estimation, the authors take a vectorization operation on difference matrix, and further enforce sparsity by reweighted l1-norm penalty. We utilize data-validation to select the regularization parameter properly. Meanwhile, a kind of symmetric grid division and refinement strategy is introduced to make the proposed algorithm effective and also to mitigate the effects of limiting estimates to a grid of spatial locations. Compared with the covariance-differencing-based multiple signal classification （MUSIC） method, the proposed is of salient features, including increased resolution, improved robustness to colored noise, distinguishing the false peaks easily, but with no requiring of prior knowledge of the number of sources.     

Sparse least mean fourth algorithm for adaptive channel estimation in low signal‐to‐noise ratio region

Both least mean square (LMS) and least mean fourth (LMF) are popular adaptive algorithms with application to adaptive channel estimation. Because the wireless channel vector is often sparse, sparse LMS‐based approaches have been proposed with different sparse penalties, for example, zero‐attracting LMS and L_p‐norm LMS. However, these proposed methods lead to suboptimal solutions in low signal‐to‐noise ratio (SNR) region, and the suboptimal solutions are caused by LMS‐based algorithms that are sensitive to the scaling of input signal and strong noise. Comparatively, LMF can achieve better solution in low SNR region. However, LMF cannot exploit the sparse information because the algorithm depends only on its adaptive updating error but neglects the inherent sparse channel structure. In this paper, we propose several sparse LMF algorithms with different sparse penalties to achieve better solution in low SNR region and take the advantage of channel sparsity at the same time. The contribution of this paper is briefly summarized as follows: (1) construct the cost functions of the LMF algorithm with different sparse penalties; (2) derive their lower bounds; and (3) provide experiment results to show the performance advantage of the propose method in low SNR region. Copyright © 2013 John Wiley & Sons, Ltd.     

不同粒径对土壤有机质含量可见—近红外光谱预测的影响

土壤有机质(SOM)是表征土壤肥力的重要指标,实现其快速准确检测可为精准农业区域管理提供有效的数据支撑。土壤粒径对SOM 的光谱预测及仪器开发有很大的影响,为了明确不同粒径对 SOM 预测的影响,分别制备了1～2,0.5～1,0.25～0.5,0.1～0.25和<0.1mm 五种均匀粒径及<1mm 混合粒径共计6种粒径土样并进行了可见-近红外(300～2 500nm)光谱数据采集。采用蒙特卡罗交叉验证分别剔除了不同粒径的异常样本,结合Savitzky-Golay卷积平滑法对光谱数据进行平滑去噪处理,比较了不同粒径样品的光谱反射率差异,并对平滑后的原始光谱 R进行倒数IR、对数 LR、一阶导数 FDR等3种光谱变换并分析与SOM 含量的相关性,基于竞争性自适应重加权算法(CARS)对光谱数据进行了特征波长提取,并结合偏最小二乘回归(PLSR)分别建立了相应的SOM 含量预测模型。结果表明,不同粒径土样的平均光谱反射率与变异系数随着粒径的减小逐渐增加,且在大于540nm 波长范围内,差异明显。随着粒径的减小,SOM含量与光谱反射率在全波段范围的相关性变化幅度愈加明显,FDR 变...     

激光拉曼光谱解谱和处理的方法研究

研究一种拉曼光谱解谱和处理的方法。以化学计量学为基础,信号处理技术为工具,配合计算机算法的数据处理方法。具体为基线校正:对拉曼光谱原始信号进行基于自适应迭代重加权惩罚最小二乘法的基线校正;平滑:对进行完基线校正的拉曼光谱信号进行基于惩罚最小二乘法的平滑;峰检测:对进行完基线校正和平滑的信号进行基于连续小波变换的峰检测。这种基于惩罚最小二乘法的光谱平滑具有快速,可以连续控制平滑度并且可以进行交叉验证得到最客观的平滑值。改善了基于非对称最小二乘法的传统基线校正方法的两个缺陷。同时,基于连续小波变换的峰检测算法可以自动地并且同时考虑峰形和峰高对峰进行检测,最大限度地降低了峰检测假阳性的概率。     

New regularization method and iteratively reweighted algorithm for sparse vector recovery

Motivated by the study of regularization for sparse problems,we propose a new regularization method for sparse vector recovery.We derive sufficient conditions on the well-posedness of the new regularization,and design an iterative algorithm,namely the iteratively reweighted algorithm(IR-algorithm),for efficiently computing the sparse solutions to the proposed regularization model.The convergence of the IR-algorithm and the setting of the regularization parameters are analyzed at length.Finally,we present numerical examples to illustrate the features of the new regularization and algorithm.     

基于稀疏脉冲反演的墙体参数研究

稀疏脉冲反演实际上是从含有噪声的雷达数据单道中计算出具有稀疏分布特性的反射系数。文中以范数约束为基础,提出L1 范数约束求解的方法,并且采用迭代重加权最小二乘法(IRLS)的优化算法,该算法具有精度高、速度快的优点,对穿墙侦查雷达数据处理后可以有效提高分辨率,获得反射系数,有助于进一步求得墙体的厚度和介电常数。最后针对不同数据的实验结果验证了算法的有效性。     

基于DWT-IRLS算法的超声断层成像方法研究

超声成像因非侵入式、成本低且实时性好而被广泛应用。超声系统需要大量的采集通道数据和较高的采样率来提高图像重建质量,导致成像耗时,系统复杂。压缩感知(compressed sensing, CS)算法能够在欠采样的条件下用较少的测量值重构出原始信号。因此,针对系统面临的采样率高,数据量大的问题,本文将CS理论中的DWT-IRLS算法应用在超声成像中,通过离散小波变换基(discrete wavelet transformation, DWT)对超声数据进行稀疏转换,对高低频系数进行采样测量,并使用迭代重加权最小二乘法(iterative reweighted least squares, IRLS)进行测量系数重构,最后对变换域系数进行DWT逆转换得到重建图像。通过实验分析,以50%原始数据重建图像效果逐渐趋于稳定,在均方误差和峰值信噪比方面进行对比分析,DWT-IRLS算法相比较于DWT-OMP、DWT-CoSamp和DCT-IRLS等重构算法,成像质量更高,细节特征更为明显。     

The constrained Fisher scoring method for maximum likelihood computation of a nonparametric mixing distribution

This paper proposes a new method for computing the nonparametric maximum likelihood estimate of a mixing distribution. It uses the Fisher scoring quadratic approximation to the log-likelihood function of the mixing proportions. At each iteration, new candidate support points are found and included, as guided by the gradient function, and bad support points are discarded, after being found redundant by optimizing the quadratic approximation. Numerical studies show that the CFS method is generally competitive with the fast and stable constrained Newton method; it may even have an advantage over the latter when the initial estimate is badly chosen.     

基于稳定竞争自适应重加权采样的光谱分析无标模型传递方法

提出了一种基于稳定竞争自适应重加权采样(stability competitive adaptive reweighted sampling,SCARS)的无标模型传递方法。利用有用信息标准即稳定度指数(定义为回归系数除以其标准偏差的绝对值)和传递后的预测均方根误差(root mean squared error of prediction,RMSEP),选择重要的、受测样参数影响不敏感的波长变量,能够消除或减少不同仪器或测量条件对样本信息反应差异,提高模型传递效果。此外,在该方法中,光谱变量被压缩、降维,从而使模型传递更稳定。采用该方法对谷物的近红外光谱分析模型在不同仪器之间进行传递研究。结果表明,该方法能消除仪器间的大部分差异,较好地实现模型传递效果。与正交信号校正法(orthogonal signal correction,OSC)、蒙特卡罗结合无用信息变量消除法(Monte Carlo uninformative variable elimination,MCUVE)、竞争自适应重加权采样法(competitive adaptive reweighted sampling,CARS)的比较表明,SCARS不仅在传递精度上能取得比OSC、MCUVE及CARS更好的效果,而且能有效地对光谱数据进行压缩,简化并优化传递过程。     

基于CARS-MIV-SVR的库尔勒香梨可溶性固体含量预测方法

为了实现库尔勒香梨依据可溶性固体含量（SSC）分级定等和按质论价,推动采后处理向标准化、产业化方向健康发展,利用高光谱成像技术研究出了一种快速、有效、无损检测库尔勒香梨SSC的方法。以表面无损伤的157个库尔勒香梨作为研究样本,应用高光谱成像采集系统获取400~1 000 nm波长范围内高光谱图像并用ENVI5.3软件提取感兴趣区域（ROI）,获得高光谱数据。采用Kennard-Stone（KS）样本集划分方法将全部样本按照2∶1的比例划分为校正集（105）和预测集（52）。对比标准变量变换（SNV）、多元散射校正（MSC）、一阶导数（FD）和二阶导数（SD）等数据预处理方法对建模精度的影响,最终选用SNV方法对光谱曲线进行平滑去噪。该研究提出竞争性自适应重加权算法与平均影响值算法的组合算法（CARS-MIV）选择特征波长。在竞争性自适应重加权算法（CARS）方法中,建模样本由蒙特卡罗算法随机选择生成,变量回归系数会随之发生变化,因而回归系数的绝对值不能全面反映变量重要性,从而影响模型检测精度。为降低这种影响,应用平均影响值（MIV）算法对选出的自变量进行二次筛选,筛选出相关性较大的变量用以建模分析,并与CARS、连续投影算法（SPA）、蒙特卡罗无信息变量消除算法（MCUVE）等经典特征波长选择算法进行比较。最后分别以全波长（FS）光谱信息和四种特征波长选择方法得出的光谱信息作为输入矢量,应用支持向量回归（SVR）建立库尔勒香梨可溶性固体含量定量预测数学模型,以校正集相关系数（R_c）、校正集均方根误差（RMSEC）、预测集相关系数（R_p）和预测集均方根误差（RMSEP）四个参数来评估模型的预测精度。比较分析发现,CARS-MIV-SVR模型效果最佳,校正集相关系数（R_c）为0.985 94,预测集相关系数（R_p）达到0.946 31,校正集和预测集均方根误差分别为0.185 85和0.403 33。结果证明：CARS-MIV特征波长选择方法能够有效增强库尔勒香梨光谱数据特征波长选择的稳定性和精确性,提高模型的预测精度。利用高光谱技术结合CARS-MIV-SVR模型能够满足库尔勒香梨可溶性固体含量测定需求,实现库尔勒香梨的分级定等和按质论价。