Simplified p-norm-like Constraint LMS Algorithm for Efficient Estimation of Underwater Acoustic Channels

Underwater acoustic channels are recognized for being one of the most difficult propagation media due to considerable difficulties such as： multipath, ambient noise, time-frequency selective fading. The exploitation of sparsity contained in underwater acoustic channels provides a potential solution to improve the performance of underwater acoustic channel estimation. Compared with the classic 10 and 11 norm constraint LMS algorithms, the p-norm-like （Ip） constraint LMS algorithm proposed in our previous investigation exhibits better sparsity exploitation performance at the presence of channel variations, as it enables the adaptability to the sparseness by tuning of p parameter. However, the decimal exponential calculation associated with the p-norm-like constraint LMS algorithm poses considerable limitations in practical application. In this paper, a simplified variant of the p-norm-like constraint LMS was proposed with the employment of Newton iteration m to approximate the decimal exponential calculation. Num simulations and the experimental results obtained in physical shallow water channels demonstrate the effectiveness of the proposed method compared to traditional norm constraint LMS algorithms.

Huber inversion-based reverse-time migration with de-primary imaging condition and curvelet-domain sparse constraint

Least-squares reverse-time migration(LSRTM) formulates reverse-time migration(RTM) in the leastsquares inversion framework to obtain the optimal reflectivity image. It can generate images with more accurate amplitudes, higher resolution, and fewer artifacts than RTM. However, three problems still exist:(1) inversion can be dominated by strong events in the residual;(2) low-wavenumber artifacts in the gradient affect convergence speed and imaging results;(3) high-wavenumber noise is also amplified as iteration increases. To solve these three problems, we have improved LSRTM: firstly, we use Hubernorm as the objective function to emphasize the weak reflectors during the inversion;secondly, we adapt the de-primary imaging condition to remove the low-wavenumber artifacts above strong reflectors as well as the false high-wavenumber reflectors in the gradient;thirdly, we apply the L1-norm sparse constraint in the curvelet-domain as the regularization term to suppress the high-wavenumber migration noise. As the new inversion objective function contains the non-smooth L1-norm, we use a modified iterative soft thresholding(IST) method to update along the Polak-Ribie re conjugate-gradient direction by using a preconditioned non-linear conjugate-gradient(PNCG) method. The numerical examples,especially the Sigsbee2 A model, demonstrate that the Huber inversion-based RTM can generate highquality images by mitigating migration artifacts and improving the contribution of weak reflection events.

Single color image super-resolution using sparse representation and color constraint

Color image super-resolution reconstruction based on the sparse representation model usually adopts the regularization norm(e.g.,L1 or L2).These methods have limited ability to keep image texture detail to some extent and are easy to cause the problem of blurring details and color artifacts in color reconstructed images.This paper presents a color super-resolution reconstruction method combining the L2/3 sparse regularization model with color channel constraints.The method converts the low-resolution color image from RGB to YCbCr.The L2/3 sparse regularization model is designed to reconstruct the brightness channel of the input low-resolution color image.Then the color channel-constraint method is adopted to remove artifacts of the reconstructed highresolution image.The method not only ensures the reconstruction quality of the color image details,but also improves the removal ability of color artifacts.The experimental results on natural images validate that our method has improved both subjective and objective evaluation.

Efficient tracker based on sparse coding with Euclidean local structure-based constraint

Abstract:Sparse coding(SC)based visual tracking(l1-tracker)is gaining increasing attention,and many related algorithms are developed.In these algorithms,each candidate region is sparsely represented as a set of target templates.However,the structure connecting these candidate regions is usually ignored.Lu proposed an NLSSC-tracker with non-local self-similarity sparse coding to address this issue,which has a high computational cost.In this study,we propose an Euclidean local-structure constraint based sparse coding tracker with a smoothed Euclidean local structure.With this tracker,the optimization procedure is transformed to a small-scale l1-optimization problem,significantly reducing the computational cost.Extensive experimental results on visual tracking demonstrate the eectiveness and efficiency of the proposed algorithm.

Compressed sensing estimation of sparse underwater acoustic channels with a large time delay spread

The estimation of sparse underwater acoustic channels with a large time delay spread is investigated under the framework of compressed sensing. For these types of channels, the excessively long impulse response will significantly degrade the convergence rate and tracking capability of the traditional estimation algorithms such as least squares （LS）, while excluding the use of the delay-Doppler spread function due to huge computational complexity. By constructing a Toeplitz matrix with a training sequence as the measurement matrix, the estimation problem of long sparse acoustic channels is formulated into a compressed sensing problem to facilitate the efficient exploitation of sparsity. Furthermore, unlike the traditional l1 norm or exponent-based approximation l0 norm sparse recovery strategy, a novel variant of approximate l0 norm called AL0 is proposed, minimization of which leads to the derivation of a hybrid approach by iteratively projecting the steepest descent solution to the feasible set. Numerical simulations as well as sea trial experiments are compared and analyzed to demonstrate the superior performance of the proposed algorithm.

Three-dimensional gravity inversion based on sparse recovery iteration using approximate zero norm

This research proposes a novel three-dimensional gravity inversion based on sparse recovery in compress sensing. Zero norm is selected as the objective function, which is then iteratively solved by the approximate zero norm solution. The inversion approach mainly employs forward modeling; a depth weight function is introduced into the objective function of the zero norms. Sparse inversion results are obtained by the corresponding optimal mathematical method. To achieve the practical geophysical and geological significance of the results, penalty function is applied to constrain the density values. Results obtained by proposed provide clear boundary depth and density contrast distribution information. The method's accuracy, validity, and reliability are verified by comparing its results with those of synthetic models. To further explain its reliability, a practical gravity data is obtained for a region in Texas, USA is applied. Inversion results for this region are compared with those of previous studies, including a research of logging data in the same area. The depth of salt dome obtained by the inversion method is 4.2 km, which is in good agreement with the 4.4 km value from the logging data. From this, the practicality of the inversion method is also validated.

稀疏约束与时间一致的背景感知相关滤波目标跟踪

背景感知滤波算法通过循环移位采集真实负样本,有效解决了边界效应.但在复杂场景例如遮挡、快速移动、背景干扰等,其较大的采样区域导致过多背景在杂波干扰,从而影响跟踪效果.针对这一问题,本文首先提取灰度HOG特征与颜色CN特征来提高目标外观模型,在基准目标函数基础上引入L1稀疏正则约束形成弹性网络以自适应筛选关键特征,增强滤波器在复杂背景下的判别能力.同时针对BACF在跟踪过程中目标快速变化,本文引入时间正则项提高滤波器抑制畸变的能力.最后,本文提出了一种独立的尺度滤波器算法,准确提供目标尺度大小.实验仿真结果表明,在公开数据集OTB-2013和OTB-2015上,本文算法较基准算法有很大提升,能够较好应对不同复杂场景下的跟踪难题.

基于重加权L1的ATpV正则化叠前反演方法

地震叠前反演能够准确获取地下储层介质的各类参数,是油气的勘探与开发中重要技术之一。然而,地震反演是典型的病态问题,为了克服此问题,通常使用正则化约束目标函数,来减轻反演问题的病态性。但是正则化约束忽略了地层边界的振幅信息,使用重加权方法可以很好地克服这一问题,更好地恢复稀疏性。提出了一种基于重加权L1的ATpV正则化叠前三参数反演方法(ATpV-L1方法),首次将重加权L1方法与ATpV方法结合,并引入到叠前反演中。采用交替方向乘子算法(ADMM)建立反演框架,对目标函数进行分块优化,有效提高了收敛速度。首先,介绍ATpV-L1方法,建立了基于ATpV-L1的叠前反演目标函数;然后,应用理论模拟数据对比新方法和ATpV方法反演结果,验证了方法的效果;最后,使用实际数据进行实验分析,进一步验证了ATpV-L1方法的反演精度及可行性。实验结果表明,提出的ATpV-L1方法可以有效恢复反演结果的稀疏性,提高反演精度。

基于全局双约束的矿井尘雾图像增强方法

为提高煤矿尘雾图像的可观测性,提出一种基于全局双约束的Retinex算法的尘雾图像增强算法(GCFCDL-Retinex)。首先,将输入图像进行内外循环,训练聚类和稀疏双重约束下的过完备字典,对图像中的噪声分量进行抑制;然后,通过Retinex算法对照度分量和反射分量进行估计及提取,并对提取的照度分量进行自适应Gamma校正;最后输出增强后的图像。研究结果表明:在煤矿井下的复杂环境中,所提出的图像增强算法能够有效提高矿井下尘雾图像的对比度和清晰度,去除真实粉尘,同时抑制图像光晕、边缘模糊的现象,增强后的图像色彩自然,视觉效果明显提升。研究结论为矿井下视频监控清晰化的工程应用提供理论依据。

双基地SAR动目标图像相位误差分析和二维稀疏重聚焦处理

双基地合成孔径雷达(Synthetic Aperture Radar,SAR)系统中,数据采集空间几何关系复杂,动目标回波距离徙动严重,重建图像中往往存在几何失真和散焦现象,需要进一步补偿运动相位误差。为此,分析并推导了双基地动目标极坐标格式图像的二维相位误差谱。根据相位误差模型建立字典,约束动目标回波谱在字典上的投影系数,进而实现杂波和动目标能量的分离和重聚焦成像。点目标仿真验证了算法的有效性。

Experimental analysis and application of sparsity constrained deconvolution

Sparsity constrained deconvolution can improve the resolution of band-limited seismic data compared to conventional deconvolution. However, such deconvolution methods result in nonunique solutions and suppress weak reflections. The Cauchy function, modified Cauchy function, and Huber function are commonly used constraint criteria in sparse deconvolution. We used numerical experiments to analyze the ability of sparsity constrained deconvolution to restore reflectivity sequences and protect weak reflections under different constraint criteria. The experimental results demonstrate that the performance of sparsity constrained deconvolution depends on the agreement between the constraint criteria and the probability distribution of the reflectivity sequences; furthermore, the modified Cauchy- constrained criterion protects the weak reflections better than the other criteria. Based on the model experiments, the probability distribution of the reflectivity sequences of carbonate and clastic formations is statistically analyzed by using well-logging data and then the modified Cauchy-constrained deconvolution is applied to real seismic data much improving the resolution.

低照度宽视场视频中微小目标相关滤波跟踪方法

此前的相关滤波目标跟踪算法,关于低照度宽视场小目标跟踪的研究工作未见报道。针对此,提出一种将图像差异检测框架与基于相关滤波器(CF)的跟踪框架相结合的算法,并引入双重滤波器对抗环境带来的不利因素。提出用l2范数的稀疏响应正则化项来抑制CF框架产生的异常波峰。在响应阶段,根据双重滤波器加权融合,预测小目标的位置。结果表明,提出算法在低照度宽视场中对小目标的快速移动、形变、运动模糊有优异的跟踪性能,并且满足实时性能。采集了全新的具有41条由鹰眼摄像头拍摄的夜晚监控序列数据集作为基准。实验结果表明本算法在DP提升了8.8%,AUC提升了7.4%,并在单个CPU上实现30.6帧每秒的实时运行。

基于双平滑函数秩近似和群稀疏的高光谱图像恢复模型

高光谱图像(HSI)具有良好的光谱识别能力,被广泛地应用于各种领域。然而,HSI在成像过程中易受到混合噪声的污染,会严重削弱后续任务的准确性,如何高质量地恢复HSI是需要解决的首要问题。目前,基于低秩先验和全变分正则化结合的HSI去噪方法取得了较好的性能,但这些方法一方面忽略了高强度条纹噪声在空间结构和光谱分布上的特征,使得噪声无法完全去除,另一方面没有考虑HSI差分图像低秩子空间的信息,不能挖掘潜在的局部空间光滑结构。为此,提出了一种基于双平滑函数秩近似和群稀疏的HSI恢复模型。首先,利用双平滑函数秩近似模型探索干净HSI和条纹噪声的低秩结构,去除结构化条纹噪声等高强度混合噪声。其次,将基于E3DTV的群稀疏正则化融入双平滑函数秩近似模型中,充分挖掘HSI差分图像的稀疏先验信息,进一步提升图像在空间恢复和光谱特征保留方面的性能。最后,设计了交替方向乘子法(ADMM)求解所提出的BSRAGS模型。仿真和真实数据实验均表明,所提模型能够有效提高图像恢复质量。

多源多精度数据融合与气动特性智能外推

在虚拟飞行试验和飞行器优化设计中,高维高精度的计算往往伴随着高昂的计算成本.代理模型可以大幅度提高计算效率,同时具备对多源多精度数据的融合能力,可基于离散试验数据点进行气动特性校正.基于此目的,将控制方程向低维空间投影,提出一种侵入式降阶的边界层修正算法,在保证精度同时高效地实现采样数据库的扩充,在此基础上构建的代理模型能够提升对飞行器阻力系数的预测精度.其次,通过在降阶模型中融合多源离散试验数据,基于最小二乘的思想引入边界约束,使得代理模型对物面压力分布特性的预测更加贴合给定的试验值.随后构建Kriging桥函数,将局部约束外推至整个物面,实现物面任意位置压力和热流分布修正值的智能匹配,为多源离散约束的融合提供一种新的算法.上述方法在二维翼型和三维钝锥以及无舵飞行器中得到验证,结果表明多精度数据融合算法构造的代理模型相比于单一精度源的代理模型预测流场残差更小,阻力系数预测更为精准,且当无黏采样的外场信息足够充足时,多精度模型预测结果可与CFD计算结果基本无异.进一步发展的多源数据融合算法和气动特性智能外推算法可充分融合离散点试验数据,改善物面压力分布和热流分布的预测结果.

基于SVM稀疏表示的类特别字典学习算法

近年来,深度学习对大规模训练样本的依赖性成为一个突出问题。在面对小样本数据集时,字典学习算法被提出作为一种解决方案。为了进一步提升字典学习在图像分类领域的竞争优势,本文提出了一种基于支持向量机的类特别字典学习算法。该算法创新性地引入了类特别系数相异性约束项。该约束项将原本独立的重建项、稀疏项和判别项融合为一个统一的学习框架,以显著提升字典的判别能力。实验证明,该模型的分类性能优于其他先进的字典学习模型。此外,本文提出将深度学习预训练与字典学习算法相结合的方式,通过实验证明该方式可以显著提升字典学习算法在大规模训练样本中的分类性能。

基于TOEPLITZ重构的压缩感知嵌套阵列DOA估计

针对传统稀疏阵列波达方向(DOA)估计算法在小快拍数、低信噪比和多信源数等条件下的估计精度不高的问题,提出了一种基于TOEPLITZ重构的压缩感知嵌套阵列DOA估计方法。首先利用TOEPLITZ重构方法将虚拟阵列的输出信号向量构建成满秩协方差矩阵,然后利用信号在空间域的稀疏性,将阵列协方差矩阵进行稀疏表示,通过噪声子空间和信号子空间的正交关系构建权值向量,对稀疏向量进行加权约束,最后通过求解最优化方程获取入射信源的DOA估计。仿真结果表明,本文方法比传统稀疏阵列DOA估计算法在低信噪比、小快拍数和多信源数下具有更好的DOA估计性能。

基于三支决策的多视图低秩稀疏子空间聚类算法

多视图子空间聚类是一种从子空间中学习所有视图共享的统一表示,挖掘数据潜在聚类结构的方法.作为一种处理高维数据的聚类方法,子空间聚类是多视图聚类领域的研究热点之一.多视图低秩稀疏子空间聚类是一种结合了低秩表示和稀疏约束的子空间聚类方法.该算法在构造亲和矩阵过程中,利用低秩稀疏约束同时捕捉了数据的全局结构和局部结构,优化了子空间聚类的性能.三支决策是一种基于粗糙集模型的决策思想,常被应用于聚类算法来反映聚类过程中对象与类簇之间的不确定性关系.本文基于三支决策的思想,设计了一种投票制度作为决策依据,将其与多视图稀疏子空间聚类组成一个统一框架,从而形成一种新的算法.在多个人工数据集和真实数据集上的实验表明,该算法可提高多视图聚类的准确性.

约束条件下线性可加空间自回归模型方法研究

[目的]针对空间依赖性问题,提出约束条件下线性可加空间自回归模型的统计推断方法。[方法]先对线性可加空间自回归模型增加线性约束条件。通过B样条基函数近似求解未知的平滑函数,并代入回归模型,生成包含B样条基函数的空间自回归模型,对此模型的损失函数进行最小化,获取约束条件下模型的稀疏估计,通过SCAD惩罚函数无偏估计较大系数。在10种约束条件下,研究了线性可加空间自回归模型估计的渐近性质,得到在最优收敛速度下进行斜率系数的估计时,函数项部分不会改变参数估计的渐近分布特性。[结果]实验结果表明,该方法不仅在理论上具有可行性,而且在实践中易于操作。相比非约束估计,约束稀疏估计更为优秀。[结论]在交通数据统计分析中,该方法具有实用性和有效性。

基于稀疏约束的最小方差无偏自适应荷载估计

提出了一种基于稀疏约束的最小方差无偏自适应荷载估计算法,该算法是对传统的最小方差无偏估计算法的改进。利用大部分结构荷载在空间稀疏的特点,通过PM(pseudo-measurement)技术对荷载向量施加了一个稀疏约束,最终无约束的最小二乘估计转换为了基于l 1范数的稀疏约束,在这样的改进下,传统算法在加速度观测下的荷载漂移问题被有效地解决,同时提高了算法荷载估计的鲁棒性。此外,对于噪声估计传统做法都是通过经验进行手动设置,在工程应用中极大不便。基于此,引入了自适应估计算法,实现了测量噪声协方差自适应估计。最后,通过1个10自由度弹簧阻尼系统和一个3层框架试验结构验证了算法的有效性。

基于多级中值滤波的电力扰动数据分离建模

电力数据中存在着大量噪声,导致正常数据与扰动数据难以分离,因此提出了基于多级中值滤波的电力扰动数据分离建模方法。选定多级中值滤波窗口,利用该窗口对电力数据进行滤波处理,保留电力数据的细节特征,实现噪声抑制。分析扰动源数据激发的问题,结合干扰源的延迟时间编码搭建电力扰动数据感知模型。根据电力扰动数据感知结果以及稀疏约束范数项,构建电力扰动数据分离模型,实现正常数据和扰动数据的分离。由实验结果可知,该方法的电力扰动数据分离效果好,且最大信噪比为41 dB,达到了研究预期。