L^s- norm Estimates of Solutions for theImhomogeneous Cauchy- Riemann Equation

In this paper,we discussed the local integral solution operators of imhomogeneous Cauchy Riemann equations on an open set with piecewise C k boundary in C n,as a generalization of the solution opertators for Leray map S(z,ζ) which do not depends holomorphic on z∈D in Koppelman formula is obtained and the L s norm estimates for the solution operators are the same as that [10] in forms.

L^p MIXED-NORM ESTIMATES FOR CONVOLUTION OPERATOR DEFINED BY SINGULAR MEASURES

Let F be a C^∞ curve in IR^n and μ be the measure induced by Lebesgue measure on F, multiplied by a smooth cut-off function. In this paper, we will prove some mixednorm estimates based on the average decay estimates of the Fourier transform of μ.

OPTIMAL ERROR ESTIMATE IN L~∞(J;L^2(Ω)) NORM OF FEM FOR A MODEL FOR COMPRESSIBLE MISCIBLE DISPLACEMENT IN POROUS MEDIA

We’ll study the FEM for a model for compressible miscible displacement in porous media which includes molecular diffusion and mechanical dispersion in one-dimensional space.A class of vertices-edges-elements interpolation operator ink is introduced.With the help of ink(not elliptic projection),the optimal error estimate in L∞(J;L2(Ω)) norm of FEM is proved.

Channel Estimation for Reconfigurable Intelligent Surface Aided Multiuser Millimeter-Wave/THz Systems

It is assumed that reconfigurable intelligent surface(RIS)is a key technology to enable the potential of mmWave communications.The passivity of the RIS makes channel estimation difficult because the channel can only be measured at the transceiver and not at the RIS.In this paper,we propose a novel separate channel estimator via exploiting the cascaded sparsity in the continuously valued angular domain of the cascaded channel for the RIS-enabled millimeter-wave/Tera-Hz systems,i.e.,the two-stage estimation method where the cascaded channel is separated into the base station(BS)-RIS and the RIS-user(UE)ones.Specifically,we first reveal the cascaded sparsity,i.e.,the sparsity exists in the hybrid angular domains of BS-RIS and the RIS-UEs separated channels,to construct the specific sparsity structure for RIS enabled multi-user systems.Then,we formulate the channel estimation problem using atomic norm minimization(ANM)to enhance the proposed sparsity structure in the continuous angular domains,where a low-complexity channel estimator via Alternating Direction Method of Multipliers(ADMM)is proposed.Simulation findings demonstrate that the proposed channel estimator outperforms the current state-of-the-arts in terms of performance.

Coarse-fine joint target parameter estimation method based on AN-RSC in OFDM passive radar

In this paper,we study the accuracy of delay-Doppler parameter estimation of targets in a passive radar using orthogonal frequency division multiplexing(OFDM)signal.A coarse-fine joint estimation method is proposed to achieve better estimation accuracy of target parameters without excessive computational burden.Firstly,the modulation symbol domain(MSD)method is used to roughly estimate the delay and Doppler of targets.Then,to obtain high-precision Doppler estimation,the atomic norm(AN)based on the multiple measurement vectors(MMV)model(MMV-AN)is used to manifest the signal sparsity in the continuous Doppler domain.At the same time,a reference signal compensation(RSC)method is presented to obtain highprecision delay estimation.Simulation results based on the OFDM signal show that the coarse-fine joint estimation method based on AN-RSC can obtain a more accurate estimation of target parameters compared with other algorithms.In addition,the proposed method also possesses computational advantages compared with the joint parameter estimation.

R^(1+1)中Maxwell-Chern-Simons-Higgs模型解的L^(∞)估计

用能量守恒估计和特征线估计的方法解决(1+1)维Maxwell-Chern-Simons-Higgs模型解的Sobolev范数增长性估计问题,给出模型有限能量解的一阶导数的L^(∞)估计,并通过改进指数增长得到解的H^(2)范数的多项式增长.

Negative Norm Estimates for Arbitrary Lagrangian-Eulerian Discontinuous Galerkin Method for Nonlinear Hyperbolic Equations

In this paper,we present the negative norm estimates for the arbitrary Lagrangian-Eulerian discontinuous Galerkin(ALE-DG)method solving nonlinear hyperbolic equations with smooth solutions.The smoothness-increasing accuracy-conserving(SIAC)filter is a post-processing technique to enhance the accuracy of the discontinuous Galerkin(DG)solutions.This work is the essential step to extend the SIAC filter to the moving mesh for nonlinear problems.By the post-processing theory,the negative norm estimates are vital to get the superconvergence error estimates of the solutions after post-processing in the L2 norm.Although the SIAC filter has been extended to nonuniform mesh,the analysis of fil-tered solutions on the nonuniform mesh is complicated.We prove superconvergence error estimates in the negative norm for the ALE-DG method on moving meshes.The main dif-ficulties of the analysis are the terms in the ALE-DG scheme brought by the grid velocity field,and the time-dependent function space.The mapping from time-dependent cells to reference cells is very crucial in the proof.The numerical results also confirm the theoreti-cal proof.

AN INTEGRAL ESTIMATE AND THE EQUIVALENT NORMS ON F(p, q, s, k) SPACES IN THE UNIT BALL

In this article, the authors give a typical integral＇s bidirectional estimates for allcases. At the same time, several equivalent characterizations on the F（p, q, s, k） space in theunit ball of Cn are given.

Minimum norm method of analyzing ill-conditioned state of design matrix in estimation of parameters

The method of condition number is commonly used to diagnose a normal matrix N whether it is ill conditioned state or not. For its shortcoming, a method to measure multi collinearity of a matrix was put forward. The method is that implement Gram Schmidt orthogonalizing process to column vectors of a design matrix A (α l ), then calculate the norms of every vector before and after orthogonalization process and their corresponding ratio, and use the minimum ratio among the group of ratios to measure the multi collinearity of A. According to the corresponding relationship between the multi collinearity and the ill conditioned state of a matrix, the method also studies and offers reference indexes weighing the ill conditioned state of a matrix based on the relative norm. The remarkable characteristics of the method are that the measure of multi collinearity has idiographic geometry meaning and clear lower and upper limit, the size of the measure reflects the multi collinearity of column vectors objectively. It is convenient to study the reason that results in the matrix being multi collinearity and to put forward solving plan according to the method which is summarized as the method of minimum norm and abbreviated as F method.

L^0-CONVEX COMPACTNESS AND RANDOM NORMAL STRUCTURE IN L^0(F,B)

Let(B,||·||)be a Banach space,(?,F,P)a probability space,and L^0(F,B)the set of equivalence classes of strong random elements(or strongly measurable functions)from(?,F,P)to(B,||·||).It is well known that L^0(F,B)becomes a complete random normed module,which has played an important role in the process of applications of random normed modules to the theory of Lebesgue-Bochner function spaces and random operator theory.Let V be a closed convex subset of B and L^0(F,V)the set of equivalence classes of strong random elements from(?,F,P)to V.The central purpose of this article is to prove the following two results:(1)L^0(F,V)is L^0-convexly compact if and only if V is weakly compact;(2)L^0(F,V)has random normal structure if V is weakly compact and has normal structure.As an application,a general random fixed point theorem for a strong random nonexpansive operator is given,which generalizes and improves several well known results.We hope that our new method,namely skillfully combining measurable selection theorems,the theory of random normed modules,and Banach space techniques,can be applied in the other related aspects.

Parameter Estimation of Multiple Frequency-Hopping Signals Based on Space-Time-Frequency Analysis by Atomic Norm Soft Thresholding with Missing Observations

In this paper,we address the problem of multiple frequency-hopping(FH)signal parameters estimation in the presence of random missing observations.A space-time matrix with random missing observations is acquired by a uniform linear array(ULA).We exploit the inherent incomplete data processing capability of atomic norm soft thresholding(AST)to analyze the space-time matrix and complete the accurate estimation of the hopping time and frequency of the received FH signals.The hopping time is obtained by the sudden changes of the spatial information,which is implemented as the boundary to divide the time domain signal so that each segment of the signal is a superposition of time-invariant multiple components.Then,the frequency of multiple signal components can be estimated precisely by AST within each segment.After obtaining the above two parameters of the hopping time and the frequency of signals,the direction of arrival(DOA)can be directly calculated by them,and the network sorting can be realized.Results of simulation show that the proposed method is superior to the existing technology.Even when a large portion of data observations is missing,as the number of array elements increases,the proposed method still achieves acceptable accuracy of multi-FH signal parameters estimation.

Mathematical Analysis of Two Approaches for Optimal Parameter Estimates to Modeling Time Dependent Properties of Viscoelastic Materials

Mathematical models for phenomena in the physical sciences are typically parameter-dependent, and the estimation of parameters that optimally model the trends suggested by experimental observation depends on how model-observation discrepancies are quantified. Commonly used parameter estimation techniques based on least-squares minimization of the model-observation discrepancies assume that the discrepancies are quantified with the L2-norm applied to a discrepancy function. While techniques based on such an assumption work well for many applications, other applications are better suited for least-squared minimization approaches that are based on other norm or inner-product induced topologies. Motivated by an application in the material sciences, the new alternative least-squares approach is defined and an insightful analytical comparison with a baseline least-squares approach is provided.

阵元失效下稀疏阵列的二维DOA估计算法

本文针对二维稀疏阵列在阵元失效条件下,因数据缺失导致虚拟阵列连续性被破坏及自由度下降的问题,提出了一种二维DOA估计算法。首先基于二维差分共阵构建虚拟阵列,然后利用解耦原子范数最小化理论,以矩阵填充的形式恢复协方差矩阵数据,实现对虚拟阵列中丢失虚拟阵元的内插,最后采用SS-MUSIC算法进行多信源的二维DOA估计。所提方法弥补了物理阵元失效所造成的影响,恢复了原始虚拟阵列的完整孔径特性,保持了虚拟阵列的自由度,从而确保了较高精度的二维DOA估计性能。仿真实验结果表明,在相同阵元数量及阵元失效情况下,本文提出的算法相比已有方法能有效地估计更多信源,并在小快拍数和低信噪比条件下表现出更高的稳健性,最大限度地保留并利用了稀疏阵列在二维DOA估计中的自由度优势。

基于低秩块Hankel矩阵正则化的阵元故障MIMO雷达DOA估计

多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达在阵元故障时虚拟阵列输出数据矩阵会出现大量的整行数据丢失,由于阵列接收数据矩阵的不完整而导致对波达方向(Direction of Arrival,DOA)的估计性能恶化。大多数低秩矩阵填充算法要求缺失数据随机分布于不完整的矩阵中,无法适用于整行缺失数据的恢复问题。为此,提出了一种基于低秩块Hankel矩阵正则化的阵元故障MIMO雷达DOA估计方法。首先,通过奇异值分解(Singular Value Decomposition,SVD)降低虚拟阵列输出矩阵的维度,以减少计算复杂度。然后,对降维数据矩阵建立基于块Hankel矩阵正则化的低秩矩阵填充模型,在该模型中将MIMO雷达降维数据矩阵排列成块Hankel矩阵并施加Schatten-p范数作为正则项。最后,结合交替方向乘子法(Alternate Direction Multiplier Method,ADMM)求解该模型,获得完整的MIMO雷达降维数据矩阵。仿真结果表明,所提方法能够有效恢复降维数据矩阵中的整行数据缺失,具有较高的DOA估计精度和实时性,在阵元故障率低于50.0%时DOA估计精度优于现有方法。

冰下非高斯噪声下的范数约束波束形成方法

为解决冰下环境中噪声模型失配导致方位估计误差较大的问题,提出了一种基于范数约束的波束形成方法。该方法通过对接收信号进行范数约束来优化数据协方差矩阵,克服非高斯噪声带来的模型失配问题,提升方位估计的准确率。信号范数约束的性能分析表明,范数约束可有效抑制非高斯噪声。仿真及试验结果表明,在冰下环境中对接收信号进行范数约束后再实施波束形成可有效提高方位估计结果的准确度和稳定性。

基于l^k范数的正则化方法及其在SAR图像处理中的应用

本文基于lk范数与稀疏性之间的关系,提出了一种基于lk范数的正则化方法,该方法在抑制信号噪声的同时, 实现了信息的高精度稀疏表示。另外,通过深入研究基于lk范数的正则化方法,本文发现了正则化方法与广义岭估计的契合之处,提出了一种新的正则化参数的选取方法,解决了正则化参数选取的瓶颈问题。文中将该方法应用于SAR图像处理的实验,实验结果表明该方法具有有效的信息稀疏表示寻优功能。

非参数核密度估计的L^r收敛

核密度估计是一种具有广泛应用领域的非参数统计方法.有关它独立情况下的大样本性质,在文献[1]中已详细介绍,本文在改进Parzen给出的一个基本引理的基础上得出非参数核密度估计的Lr收敛.

基于不动点深度学习的IRS辅助毫米波移动通信系统全信道估计

将智能反射面(IRS)与大规模MIMO结合能够保证和提高毫米波通信系统性能。针对基站(BS)-用户直连信道与用户-IRS-BS反射信道混叠场景,该文提出一种自适应的全信道估计方法。首先,引入辅助变量,采用原子范数将直连信道与反射信道的稀疏角度域子空间进行关联;然后,利用原子范数最小化将全信道估计问题建模为连续角度域稀疏矩阵重建规划;最后,基于不动点深度学习网络设计低复杂度的问题求解算法。该算法不仅能够克服传统基于模型解法中非线性估计算子对先验知识的依赖还可根据移动场景变化自适应调节算法复杂度。仿真结果表明,所提算法能够避免传统时分估计策略引起的差错传播效应,具有更高的估计精度和更低的复杂度。

一种基于联合加权和截断的毫米波大规模MIMO信道估计

提出一种联合加权和截断核范数的毫米波大规模多输入多输出(MIMO)信道估计算法。针对毫米波大规模MIMO信道估计问题中训练和反馈开销大的问题,首先利用毫米波信道天线角度域稀疏的特性,把信道估计问题转化为低秩矩阵恢复问题。采用一种有效而灵活的秩函数——联合加权截断核范数作为核范数的松弛,构造出一种新的矩阵恢复模型用于信道估计问题,以最小化加权截断核范数为优化目标,并利用交替优化框架求解。仿真结果表明,该方法可以有效地提高信道估计的精度,并且具有可靠的收敛性。

几类k阶Stein函数的L^p模估计

提出了两类k(k∈N )阶Stein函数,在讨论其与k阶Littlewood-Paley函数的关系的基础上,建立了2≤p<∞时两类k阶Stein函数的Lp模估计.