Application of wavelet support vector regression on SAR data de-noising

A new filtering method for SAR data de-noising using wavelet support vector regression (WSVR) is developed.On the basis of the grey scale distribution character of SAR imagery,the logarithmic SAR image as a noise polluted signal is taken and the noise model assumption in logarithmic domain with Gaussian noise and impact noise is proposed.Based on the better performance of support vector regression (SVR) for complex signal approximation and the wavelet for signal detail expression,the wavelet kernel function is chosen as support vector kernel function.Then the logarithmic SAR image is regressed with WSVR.Furthermore the regression distance is used as a judgment index of the noise type.According to the judgment of noise type every pixel can be adaptively de-noised with different filters.Through an approximation experiment for a one-dimensional complex signal,the feasibility of SAR data regression based on WSVR is confirmed.Afterward the SAR image is treated as a two-dimensional continuous signal and filtered by an SVR with wavelet kernel function.The results show that the method proposed here reduces the radar speckle noise effectively while maintaining edge features and details well.     

联合高斯回归的平方根UKF方法

针对传统的滤波方法容易受系统动态模型不确定性和噪声协方差不准确的限制这一问题,提出一种将高斯过程回归融入平方根不敏卡尔曼滤波（unscented Kalam filter,UKF）算法中的滤波算法。该算法用高斯过程对训练数据进行学习,得到动态系统的回归模型及系统噪声的协方差;采用标准的平方根UKF算法,状态方程和观测方程,相应的噪声协方差由高斯过程实时自适应调整。将应用于飞行器SINS/GPS组合导航,结果表明,该方法能够自适应系统噪声,收敛速度快,导航精度高。     

一种基于SVR的SAR图像自适应滤波算法

针对合成孔径雷达（synthetic aperture radar, SAR）图像相干斑噪声抑制问题,提出了一种基于支持向量回归（support vector regression, SVR）分析的空间域自适应滤波方法。将SAR图像看做连续二维函数,利用SVR方法对其进行逼近。基于图像的逼近结果描述像素关联性,并基于关联性破坏程度对噪声进行类型分析,对不同类型的噪声采取确定性的抑制算法。为了保证精度,选择小波核函数构建支持向量回归机。实验结果表明了该方法的有效性和对经典方法的改进。     

复杂曲面拟合的相关向量机模型及其泛化能力

介绍相关向量机回归(RVM,Relevance Vector Machine)的基本原理。分析采用高斯径向基核函数时,核函数参数与模型性能之间的关系,并给出核函数参数选择建议。针对一个复杂非线性函数,采用均匀网格取样,并分别增加正态分布噪声和均匀分布噪声,分析RVM回归分析的拟合和泛化能力并与支持向量机(SVM,Support Vector Machine)回归模型作了比较。结果表明,SVM对样本数据的拟合能力优于RVM。但对于非训练样本,RVM的泛化能力要优于SVM,而且RVM模型更加稀疏,且在给出预测值的同时能够给出预测值的置信区间。     

我国通货膨胀的混合回归和时间序列模型

回归模型的残差项反映了对被解释变量有影响但未列入解释变量的因素所产生的噪音 ,这部分噪音可由时间序列模型进行拟合 .本文对通货膨胀建立了一个混合回归和时间序列模型 ,并将该模型的预测结果与单纯用回归模型的预测结果进行了比较.     

基于自适应参数回归的非局部图像滤波算法

针对图像加性高斯白噪声,提出一种优化的自适应参数滤波算法。该算法以非局部欧氏中值(non-local Euclidean medians, NLEM)滤波算法为基础,根据含噪图像梯度幅值在一定噪声范围内服从Rayleigh分布这一特性,求得以梯度幅值和噪声标准差为自变量的二元自适应滤波参数,并将它引入到邻域的权值计算中。其次,噪声的变化影响着l_p范数回归的选择,在一定范围内以噪声标准差为自变量对参数p进行多项式拟合,得到自适应l_p范数回归。在自适应滤波参数基础上,用自适应l_p范数回归进一步改进NLEM滤波算法的l₁范数回归。所选图像的实验结果表明,本文算法在一定噪声范围内不但获得满意的去噪效果,而且有效地减少人机交互程度。     

基于高斯过程回归的平方根UPF算法

针对系统动力学模型不准确可能导致滤波精度下降,以及系统状态协方差阵可能出现的负定性问题,提出一种新的高斯过程回归平方根分解无迹粒子滤波(Gaussian process regression square-root decomposition unscented particle filter,GPSR-UPF)算法。在该算法中,采用高斯过程回归求取UPF的重要性密度函数。当系统模型不准确时,通过高斯过程回归学习训练数据,进而获取系统的回归模型及系统噪声协方差,同时引入平方根变换抑制系统状态协方差阵的负定性。将提出的GPSR- UPF算法应用到捷联惯导/全球定位系统(strapdown inertial navigation system / global positioning system, SINS/GPS)组合导航系统中进行仿真验证。结果表明,所提出滤波算法的性能优于基本的无迹粒子滤波算法,能提高组合导航系统的解算精度。     

基于贝叶斯准则水平投影优化的目标检测算法

为了进一步提高跟踪系统中目标检测的精准性,适应不同的环境变化,对检测阈值的选取和图像的去噪提出了新的算法。结合似然函数和贝叶斯判别准则理论,根据信号和噪声的统计分布规律,得到一个自适应的动态阈值。利用该阈值对二值化图像的水平投影进行优化处理,去除噪声,得到待检测的目标。实验结果表明,该方法克服了目标完整性和抑制噪声之间的矛盾,可以有效地改善光线变化带来的噪声影响,在不同的场景情况下都可以得到满意的检测结果。     

STRONG UNIFORM CONSISTENCY RATES FOR CONDITIONAL FUNCTION ESTIMA TORS WITH ■-MIXING SAMPLE

Strong uniform consistency rates are given for kernel type estimatorsof the conditional function with (?)-mixing sample.Especially,for nonparametricestimators of kernel density,the regression function when Y is bounded,conditionaldf's,L-smoothing and M-smoothing,we obtain the same rate O((n/log n)~(-1/3))as in the i.i.d.sample established by H(?)rdle,Janssen and Serfling.     

基于PSO的SVR参数优化选择方法研究

支持向量回归机(SVR)模型的拟合精度和泛化能力取决于其相关参数的选取,因此提出了基于粒子群(PSO)算法的SVR参数优化选择方法;并以不同噪声影响下的sinc函数和实际发酵过程产物浓度的SVR模型为对象,将提出的PSO优化参数方法与现有的交叉验证法、留一法进行比较。仿真结果表明:该PSO优化SVR参数方法可行、有效,由此得到的SVR模型具有更好的学习精度和推广能力。     

模糊偏最小二乘支持向量机的应用研究

基于偏最小二乘回归法和模糊隶属度函数,提出了一种模糊偏最小二乘支持向量机.传统最小二乘支持向量机引入模糊加权系数后,可以根据训练样本点的情况调整折衷系数,有效地提高了最小二乘支持向量机的抗噪性能.同时利用偏最小二乘回归法,克服了求解线性回归方程中自变量向量间的多重相关性问题.利用 sinc 函数对该建模方法进行了测试,并进一步对铜转炉吹炼时间的预测问题进行了仿真研究.仿真结果表明,该建模方法具有预测准确、跟踪性能好的优点.     

超长波天文观测微卫星宽带有源天线设计

月球轨道超长波天文观测微卫星(path finder of discovering the sky at the longest wavelengths,DSL-P)是国际首次开展绕月轨道超长波干涉技术验证实验。DSL-P工作频率1~30 MHz,其关键技术之一为宽带有源天线。介绍了DSL-P宽带有源天线的方案,重点分析其噪声特性,通过仿真和测试获取有源天线的噪声性能,分析宽带有源天线对系统设计指标的满足度。研究表明,用于超长波观测的宽带有源天线并不是传统意义上的被动天线和前置放大器的组合,其主要作用是阻抗匹配,并可兼顾噪声要求。     

自校正解耦信息融合Wiener状态预报器

对含未知噪声方差阵的多传感器系统,用现代时间序列分析方法,基于滑动平均新息模型的在线辨识和求解相关函数矩阵方程组,可得到白噪声方差阵的在线估值器。在按状态分量标量加权线性最小方差最优信息融合准则下,提出了一种自校正解耦信息融合Wiener状态预报器,实现了状态分量的自校正解耦局部Wiener预报器和自校正解耦融合Wiener预报器。用动态误差系统稳定性分析方法证明了该预报器的收敛性,即若滑动平均新息模型参数估计是一致的,将收敛于噪声方差阵已知时的最优解耦信息融合Wiener状态预报器。一个带三传感器的目标跟踪系统的仿真例子说明了其有效性。     

α稳定分布噪声下基于稳健S变换的LFM信号参数估计

S变换由短时傅里叶变换发展而来,克服了短时傅里叶变换窗长固定、不能同时展现信号高频及低频的缺点,但在脉冲性较强的α稳定分布噪声下,该方法性能退化甚至失效。对此,基于广义柯西分布,构造了一类可有效应用于强脉冲噪声环境的损失函数,并详细分析了其影响函数的稳健性。在此基础上,根据最大似然估计理论和S变换,提出了一种稳健S变换方法。该方法以S变换作为初始值,采用最大似然估计方法在时频域迭代得到,在保留S变换窗长选取灵活等优点的同时,进一步提高了S变换的时频聚集性。仿真实验表明,在处理脉冲噪声环境下的线性调频信号时,与传统的基于Myriad滤波、Meridian滤波等多种非线性滤波的方法相比,提出的稳健S变换不仅能有效抑制脉冲噪声,且在脉冲性较强的α稳定分布噪声环境下,具有良好的鲁棒性和优良的线性调频信号参数估计性能。     

基于双重群组套索的高维空间多值处置效应估计

罗宾因果推断模型在非实验数据分析中具有重要地位,但对高维数据分析,古典低维空间处置效应估计量往往表现欠佳.本文结合高维空间下的双重选择估计与群组套索回归,提出一种估计高维稀疏空间下多值处置效应的双重群组套索估计方法.数值模拟发现,对于因果参数估计,双重群组套索估计的经验功效接近理论值,而预测性套索回归则存在较大的功效偏差.对教育生产函数的案例研究发现,该方法可以有效地从多个备选控制变量中选出正确的控制变量,仅有一个噪声变量被错误选择.     

基于自适应分割与偏移场估计的活动轮廓模型

针对传统活动轮廓模型无法快速、准确、强鲁棒性地分割灰度不均匀图像的问题,提出了偏移场估计与图像分割相结合的新型混合活动轮廓模型。首先,通过对图像进行模糊聚类分析,提出带有模糊隶属度函数的新型偏移场估计模型,提高了模型对图像灰度信息的估计与提取能力。其次,利用图像信息熵构造了自适应尺度算子(adaptive scaling operator, ASO),改善了模型的分割效率及对初始轮廓和噪声的鲁棒性。最后,通过将偏移场估计模型和ASO融入到能量泛函中,提出新型混合活动轮廓模型。实验结果表明,该模型不但对初始轮廓和不同种类噪声具有较强的鲁棒性,而且对不同程度的灰度不均匀图像具有较高的分割准确度与分割效率。     

Heteroscedasticity Detection and Estimation with Quantile Difference Method

When dealing with regression analysis, heteroscedasticity is a problem that the authors have to face with. Especially if little information can be got in advance, detection of heteroscedasticity as well as estimation of statistical models could be even more difficult. To this end, this paper proposes a quantile difference method (QDM) that can effectively estimate the heteroscedastic function. This method, being completely free from the estimation of mean regression function, is simple, robust and easy to implement. Moreover, the QDM method enables the detection of heteroscedasticity without any restrictions on error terms, consequently being widely applied. What is worth mentioning is that based on the proposed approach estimators of both mean regression function and heteroscedastic function can be obtained. In the end, the authors conduct some simulations to examine the performance of the proposed methods and use a real data to make an illustration.     

DENOISING AND HARMONIC DETECTION USING NONORTHOGONAL WAVELET PACKETS IN INDUSTRIAL APPLICATIONS

New industrial applications call for new methods and new ideas in signal analysis. Wavelet packets are new tools in industrial applications and they have just recently appeared in projects and patents. In training neural networks, for the sake of dimensionality and of ratio of time, compact information is needed. This paper deals with simultaneous noise suppression and signal compression of quasi-harmonic signals. A quasi-harmonic signal is a signal with one dominant harmonic and some more sub harmonics in superposition. Such signals often occur in rail vehicle systems, in which noisy signals are present. Typically, they are signals which come from rail overhead power lines and are generated by intermodulation phenomena and radio interferences. An important task is to monitor and recognize them. This paper proposes an algorithm to differentiate discrete signals from their noisy observations using a library of nonorthonormal bases. The algorithm combines the shrinkage technique and techniques in regression analysis using Shannon Entropy function and Cross Entropy function to select the best discernable bases. Cosine and sine wavelet bases in wavelet packets are used. The algorithm is totally general and can be used in many industrial applications. The effectiveness of the proposed method consists of using as few as possible samples of the measured signal and in the meantime highlighting the difference between the noise and the desired signal. The problem is a difficult one, but well posed. In fact, compression reduces the level of the measured noise and undesired signals but introduces the well known compression noise. The goal is to extract a coherent signal from the measured signal which will be “well represented” by suitable waveforms and a noisy signal or incoherent signal which cannot be “compressed well” by the waveforms. Recursive residual iterations with cosine and sine bases allow the extraction of elements of the required signal and the noise. The algorithm that has been developed is utilized as a filter to extract features for training neural networks. It is currently integrated in the inferential modelling platform of the unit for Advanced Control and Simulation Solutions within ABB’s industry division. An application using real measured data from an electrical railway line is presented to illustrate and analyze the effectiveness of the proposed method. Another industrial application in fault detection, in which coherent and incoherent signals are univocally visible, is also shown.     

三维AoA目标跟踪的二次约束卡尔曼滤波算法

在基于到达角(angle of arrival, AoA)的三维目标跟踪中, 伪线性卡尔曼滤波具有稳定性高和计算复杂度低的优点, 但是严重的偏差问题使其跟踪精度迅速下降。针对该问题, 提出一种二次约束卡尔曼滤波(quadratic constraint Kalman filter, QCKF)算法。首先引入涉及所有观测噪声项的增广矩阵, 然后建立与线性卡尔曼滤波等价的目标函数并且附加含有二次项的约束条件, 以此降低偏差影响, 实现更准确的状态更新。QCKF算法采用广义特征值分解求解约束优化问题, 无法直接通过状态更新表达式推导其协方差矩阵, 因此利用约束条件以及矩阵扰动方法完成协方差矩阵更新。仿真分析表明, QCKF算法相较于其他非线性滤波算法具有更优的跟踪性能, 不仅在低噪声条件下可达到后验克拉美罗下界, 而且当噪声严重时能够显著降低跟踪误差, 并且计算开销不高。     

基于滑动窗口的新息自适应组合导航算法

针对新息自适应滤波算法噪声跟踪精度和跟踪灵敏度相互矛盾导致的窗口宽度选取困难问题,提出了一种基于滑动窗口的新息自适应组合导航算法,该方法通过设计噪声统计特性梯度检测函数、敏感噪声统计特性的实际变化情况,利用窗口自适应函数实时计算窗口宽度,使得窗口在预设区间内自适应滑动,以适应实际噪声的变化。仿真实验表明,基于滑动窗口的新息自适应组合导航算法可以有效跟踪噪声统计特性的实时变化,可同时兼顾自适应跟踪精度和跟踪灵敏度。