Polarimetric super-resolution algorithm for radar range imaging via spatial smoothing processing

A full-polarimetric super-resolution algorithm with spatial smoothing processing is presented for one-dimensional（1-D）radar imaging.The coherence between scattering centers is minimized by using spatial smoothing processing（SSP）.Then the range and polarimetric scattering matrix of the scattering centers are estimated.The impact of different lengths of the smoothing window on the imaging quality is mainly analyzed with different signal-to-noise ratios（SNR）.Simulation and experimental results show that an improved radar super-resolution range profile and more precise estimation can be obtained by adjusting the length of the smoothing window under different SNR conditions.

ON TWO-DIRECTION SPATIAL SMOOTHING FOR ESTIMATION ANGLE-OF-DIRECTION OF COHERENT SIGNALS

Based on Evans’spatial smoothing preprocessing scheme,a new approach calledtwo-direction spatial smoothing preprocessing method is presented.It is proved that the decorre-lation,the effective aperture and the maximum number of distinguishable coherent signals(whenarray size is given)of the new method are better than those of the Evans’method.Simulationresults give a comparison between the eigenvector spectrums produced by the two methods.

Analytic Smoothing Effect of the Time Variable for the Spatially Homogeneous Landau Equation

In this work,we study the Cauchy problem of the spatially homogeneous Landau equation with hard potentials in a close-to-equilibrium framework.We prove that the solution to the Cauchy problem enjoys the analytic regularizing effect of the time variable with an L^(2)initial datum for positive time.So that the smoothing effect of the Cauchy problem for the spatially homogeneous Landau equation with hard potentials is exactly same as heat equation.

MODE SPACE SMOOTHING ALGORITHM TO ESTIMATE DOA OF COHERENT SIGNALS IMPINGING ON UNIFORM CIRCULAR ARRAY

A novel method to estimate DOA of coherent signals impinging on a uniform circular array( UCA) is presented in this paper. A virtual uniform linear array (VULA) is first derived by using spatial DFT technique, transforming the UCA from element space to phase mode space to obtain the properties of ordinary ULA, and then the well known spatial smoothing technique is applied to the VULA so that the lost rank of covariance matrix due to signal coherence can be retrieved. This method makes it feasible to use the simple MUSIC algorithm to estimate DOA of coherent signals impinging on a UCA without heavy computation burden. Simulation results strongly verify the effectiveness of the algorithm.

Spatially smoothed quaternion-Capon beamforming in the presence of coherent interferences

The performance the quaternion-Capon（ Q-Capon） beamformer degraded when suppressing the interferences that are coherent with the signal of interest（ SOI）. To tackle the problem,the spatial smoothing technique is adopted in quaternion domain to decorrelate the interferences by using linearly and uniformly spaced two-component electromagnetic vector-sensors. By averaging several translational invariant subarray quaternion covariance matrices,the quaternion spatial smoothing is performed to prevent the SOI cancellation phenomena caused by the presence of coherent interferences. It is demonstrated that the quaternion spatial smoothing Q-Capon beamformer can suppress the coherent interferences remarkably while the computational cost is lower than the complex domain long vector spatial smoothing counterpart. Theoretical analyses and simulation results validate the efficacy of the spatially smoothed Q-Capon beamformer in terms of coherent interference suppression capability.

The Method of Spatially Smoothed Seismicity Parameters in Consideration of Seismotectonic Background and Its Application to Seismic Hazard Estimation

In this study, the North China seismic region was selected as the study area, and evaluation of seismic hazard using the spatial smoothing seismicity model was performed. Firstly, the study area is divided into grids, and some parameters （e. g. b-value, Mo, Me, azimuth and M-L relationship ） for each seismotectonic model were assigned. Secondly, using elliptical smoothing based on a seismotectonic background model, the statistical earthquake incidence rate in each grid is successively calculated. Lastly, the relevant ground motion attenuation relationship is chosen to assess seismic hazard of general sites. The maps for the distribution of horizontal peak ground acceleration with 10% probability of exceedance in 50 years were obtained by using the seismic hazard analysis method based on grid source. This seismicity model simplifies the methodology of probabilistic seismic hazard analysis, especially appropriate for those places where seismic tectonics is not yet clearly known. This method can provide valuable references for seismic zonation and seismic safety assessment for significant engineering projects.

部分线性变系数空间自回归模型的惩罚轮廓拟最大似然方法

主要研究了部分线性变系数空间自回归模型的变量选择问题。结合拟最大似然方法、局部线性光滑方法以及一类非凸罚函数,提出了一个变量选择方法用于同时选择该模型的参数部分中重要解释变量和估计相应的非零参数。大量模拟研究表明,所提出的变量选择方法具有满意的有限样本性质,并且关于空间权矩阵的稀疏度、空间相关强度、系数函数的复杂度以及误差分布的非正态性非常稳健。特别地,当样本容量较大且罚函数选择合适时,即使解释变量的相关性较强或者模型中含有较多不重要解释变量,所提出的变量选择方法仍然具有比较满意的有限样本性质。通过分析波士顿房屋价格数据考察了所提出的变量选择方法的实际应用效果。

基于北斗GEO卫星观测的桂林地区TEC空间梯度研究

小尺度的总电子含量(TEC)梯度异常会显著影响区域电离层模型以及地基增强系统(GBAS)的精度。因此,为了提高区域电离层模型的准确性和GBAS导航定位的精度,深入研究小尺度的TEC空间梯度显得尤为重要。为了研究中小尺度的空间梯度,利用2019—2022年间桂林地区全球卫星导航系统(GNSS)接收机的北斗地球静止轨道(GEO)卫星数据,对中国低纬地区(23°N 107.9°~113.1°E)电离层的TEC空间梯度进行研究分析。根据3颗北斗GEO卫星对地形成的3个相邻穿刺点(从西至东,依次编号为C02,C03,C01)的TEC观测值,构成C02-C03与C03-C01两个约为300 km尺度区域(107.9°~110.3°E,110.3°~113.1°E)的TEC梯度观测。统计结果显示,C02-C03区域的空间梯度基本为西向;而C03-C01区域的空间梯度在1~4月与9~12月为东向,5~8月为西向。这表明2个相邻区域的空间梯度在季节变化上存在差异,揭示了中小尺度空间梯度具有显著的局地效应。此外,地方时差并非空间梯度事件的主要原因。在太阳活动高年,背景TEC较高,空间梯度事件更易发生。梯度事件发生率的半年变化是TEC半年异常的体现。空间梯度事件在不同地磁条件下的发生率相似。

基于矩阵重构和酉变换的DOA估计

为了提高重构相干信号测向算法的估计性能,降低算法运算量,提出了一种基于矩阵重构和酉变换方法的酉矩阵重构算法。该算法首先通过酉变换将阵列接收数据从复值计算转换为实值计算,使计算量大大降低;然后计算阵列协方差矩阵并进行特征值分解得到信号子空间,再将信号子空间重构为Toeplitz矩阵实现解相干并再次进行酉变换;最后通过特征值分解得到信号子空间并使用最小二乘法实现波达方向(direction of arrival,DOA)估计。相比于改进的旋转不变性的信号参数(estimation of signal parameters via rotational invariance techniques-like,ESPRIT-Like)算法和空间平滑处理算法,由于消除了噪声影响、构造了Toeplitz矩阵以及充分利用了数据的共轭信息,该算法的估计精度更高、具有更高的运算效率且在ESPRIT-Like算法失效的条件下新算法仍能有效估计DOA。本文算法的运行时间是ESPRIT-Like算法的71.2%,实验结果证明了该方法的有效性和真实性。

基于空间域图像生成和混合卷积神经网络的配电网故障选线方法

传统的配电网故障选线方法大多基于一维零序电流序列构建故障诊断模型,单一的诊断模型往往限制了故障特征的深层挖掘。为了提高故障选线的准确率,提出一种基于空间域图像和混合卷积神经网络的配电网故障选线方法。首先,利用优化的降噪光滑模型对零序电流信号进行降噪处理,减少外界环境的电磁干扰。其次,利用对称希尔伯特变换将一维时域信号转成二维空间域图像,图像的颜色、形状和纹理特征能够充分反映当前系统的运行状态。最后,将一维时域信号和二维空间域图像同步作为混合卷积神经网络的输入,充分挖掘系统的故障特征,利用Sigmoid函数实现故障选线。在辐射状配电网、IEEE-13节点模型、IEEE-34节点、StarSim仿真平台上模型上进行了实验验证。实验结果表明,该选线方法可以有效克服传统方法过度依赖主观特征选择、抗噪性能差等问题,能够在高阻接地、采样时间不同步、两点接地故障等极端情况下可靠地筛选出故障线路。

低快拍下混合信号DOA快速估计算法

实时定位移动设备在电子对抗系统中至关重要,其性能主要取决于波达角(direction of arrival,DOA)的估计速度。低快拍是快速DOA估计的先决条件。目前基于稀疏重构算法的DOA估计具有适应低快拍的优势,但估计精度受限于初始观测矩阵,且估计速度受限于观测矩阵高维度的多次迭代。为此,提出一种空间差分矩阵和稀疏重构耦合的低快拍下高精度快速估计算法。首先利用空间差分矩阵消除非相干信号和噪声对相干信号估计结果的影响,提升初始观测矩阵的准确度;然后对完备字典做前后空间平滑处理,克服高维度信号处理复杂难题,实现快速估计;最后分别估计非相干信号和相干信号。仿真验证结果表明,相比稀疏重构方法,所提方案初值敏感度显著降低,在保障精度相当甚至小幅度提升的前提下,运行时间复杂度降低50%以上。

基于L型阵的相干信号二维DOA估计方法

文中提出一种二维空间相干信号波达方向(Direction of Arrival,DOA)估计方法。该方法首先对L形阵接收数据协方差矩阵进行共轭重构,将原协方差矩阵的平方与重构矩阵的平方相加后求均值,得到新协方差矩阵。然后采用前后向空间平滑技术对新协方差矩阵进行预处理,最后通过Root-MUSIC算法进行DOA估计。仿真实验和湖试数据分析结果表明,与常规方法相比,文中方法避免了谱峰搜索,减小了计算量,提高了相干信号DOA估计的分辨成功概率和估计精度,具有较高的工程应用价值。

基于NPP-VIIRS的胶东经济圈GDP时空演变及空间化模拟研究

为研究胶东经济圈经济发展的空间差异,以2012-2022年NPP-VIIRS夜间灯光数据和GDP数据为数据源,运用探索性空间分析方法探索GDP时空演变特征,采用指数平滑、空间建模和逐像元线性矫正等方法进行GDP空间化模拟和预测.结果表明:1)胶东经济圈最佳夜间灯光指数和GDP高度相关,有相似的空间分布和演变规律;2)经济欠活跃型区域逐步缩减,经济一般型区域空间分布由带状逐渐转为片状,GDP的空间聚集性趋于减弱;3)GDP热点区主要在青岛的黄岛区和胶州市,冷点区主要在潍坊的部分县区且逐渐收缩;4)指数平滑后的夜间灯光数据与GDP相关性显著增强,更适合进行拟合;5)GDP空间化模拟相对误差小于1%,模拟效果较好;6)高密度GDP主要分布在沿海县区和城市中心城区,并在各县区的交通线节点上零星出现.

基于运动平台中心对称阵的相干信源DOA估计

当存在多径问题时,圆阵的空间谱测向性能会严重下降。对此,提出一种基于运动平台的中心对称阵平滑空间谱估计算法。该算法利用阵列在空间上的平移,得到多个平移后的协方差矩阵;同时利用中心对称阵的对称性,将接收数据反向处理,获得虚拟阵列数据的协方差矩阵。之后对所有协方差进行叠加,可获得一个新的满秩的协方差矩阵,实现解相干处理,完成信号的来波方向估计。仿真实验验证了该方法的有效性,并具备较大的工程应用价值。

基于FMCW雷达的多目标生命信号检测方法

针对调频连续波(FMCW)雷达工作带宽有限,难于对多目标生命信号准确检测的问题,提出了一种基于前后向空间平滑多重信号分类(FBSS-MUSIC)算法的FMCW雷达多目标生命信号检测方法。首先对中频信号进行距离维快速傅里叶变换(FFT)得到距离剖面矩阵,然后通过FBSS-MUSIC算法进行高分辨率距离估计从而确定每个目标所在的距离单元,接着根据每个目标所在的距离单元进行距离积分后提取每个目标对应的相位信号,使用变分模态分解(VMD)算法分解相位信号,通过模态判别准确重构呼吸和心跳信号,最后对生命信号进行短时傅里叶变换得到呼吸和心跳信号频率。实测数据结果表明,相比于距离维FFT,本文提出的算法能够显著提高FMCW雷达的距离分辨率,能够实现多个目标的准确定位并提取其生命体征信息。

基于非局部低秩和全变分的多光谱图像去噪算法

在成像过程中,多光谱图像(MSI)通常会受到高斯噪声的污染,从而影响MSI的后续应用。为了去除高斯噪声,通过考虑沿光谱的全局相关性(GCS)和跨空间的非局部自相似性(NSS),提出了一种新的基于张量的去噪方法。为了同时捕获非局部相似性和光谱相关性,MSI首先被分割成重叠的三维全波段块,通过聚类算法将相似的块进行分组。再将每个三维全波段块展开成矩阵,然后把组内的相似块级联成三阶张量,利用张量核范数对该低秩张量进行正则化约束。为了避免这一操作产生的振铃效应,利用三维加权总变分探索光谱—空间平滑性。仿真实验表明:所提算法可以有效地利用内在的GCS和NSS知识,能够从退化的MSI中恢复出更加精细的信息,在综合的量化性能指标下优于对比方法。

Spatial variability effect of internal friction angle on the post-failure behavior of landslides using a random and non-Newtonian fluid based SPH method

This study proposed a random Smoothed Particle Hydrodynamics method for analyzing the post-failure behavior of landslides,which is based on the Karhunen-Loeve(K-L) expansion,the non-Newtonian fluid model,and the OpenMP parallel framework.Then,the applicability of this method was validated by comparing the generated random field with theoretical result and by simulating the post-failure process of an actual landslide.Thereafter,an illustrative landslide example was created and simulated to obtain the spatial variability effect of internal friction angle on the post-failure behavior of landslides under different coefficients of variation(COVs) and correlation lengths(CLs).As a conclusion,the reinforcement with materials of a larger friction angle can reduce the runout distance and impact the force of a landslide.As the increase of COV,the distribution range of influence zones also increases,which indicates that the deviation of influence zones becomes large.In addition,the correlation length in Monte Carlo simulations should not be too small,otherwise the variation range of influence zones will be underestimated.

NOVEL SPECTRUM SENSING METHOD BASED ON THE SPATIAL SPECTRUM FOR COGNITIVE RADIO SYSTEMS

Spectrum sensing is the key problem for Cognitive Radio(CR) systems.A method based on the Peak-to-Average Amplitude-Ratio(PAAR) of the Spatial Spectrum(SS) of the received signals is proposed to sense the available spectrum for the cognitive users with the help of the multiple antennas at the receiver of the cognitive users.The greatest advantage of the new method is that it requires no information of the noise power and is free of the noise power uncertainty.Both the simulation and the analytical results show that the proposed method is robust to noise uncertainty,and greatly outperform the classical Energy Detector(ED) method.

Temporal Correlation-Based Spatial Filtering of Rician Noise for Functional MRIs

A novel correlation-based filter is presented for de-noising functional magnetic resonance imaging (fMRI) data.Temporal correlation-based exponential weights are defined for spatial smoothing of the data,with bias reduction using estimated noise variance.The proposed scheme is tested on simulated and real fMRI data.Finally,the results are compared with conventional filters.The method is found to be effectively suppressing the Rician noise in fMRI data,while improving the SNR.

A Spatial-Nonparametric Approach for Prediction of Claim Frequency in Motor Insurance

Spatial modeling has largely been applied in epidemiology and disease modeling. Different methods such as Generalized linear models (GLMs) have been made available to prediction of the claim frequencies. However, due to heterogeneity nature of policies, the methods do not generate precise and accurate claim frequencies predictions;these parametric statistical methods extensively depend on limiting assumptions (linearity, normality, independence among predictor variables, and a pre-existing functional form relating the criterion variable and predictive variables). This study investigates how to derive a spatial nonparametric model estimator based on smoothing Spline for predicting claim frequencies. The simulation results showed that the proposed estimator is efficient for prediction of claim frequencies than the kernel based counterpart. The estimator derived was applied to a sample of 6500 observations obtained from Cooperative Insurance Company, Kenya for the period of 2018-2020 and the results showed that the proposed method performs better than the kernel based counterpart. It is worth noting that inclusion of the spatial effects significantly improves the estimator prediction of claim frequency.