Adaptive filtering of GOCE-derived gravity gradients of the disturbing potential in the context of the space-wise approach

Filtering and signal processing techniques have been widely used in the processing of satellite gravity observations to reduce measurement noise and correlation errors. The parameters and types of filters used depend on the statistical and spectral properties of the signal under investigation. Filtering is usually applied in a non-real-time environment. The present work focuses on the implementation of an adaptive filtering technique to process satellite gravity gradiometry data for gravity field modeling. Adaptive filtering algorithms are commonly used in communication systems, noise and echo cancellation, and biomedical applications. Two independent studies have been performed to introduce adaptive signal processing techniques and test the performance of the least mean-squared (LMS) adaptive algorithm for filtering satellite measurements obtained by the gravity field and steady-state ocean circulation explorer (GOCE) mission. In the first study, a Monte Carlo simulation is performed in order to gain insights about the implementation of the LMS algorithm on data with spectral behavior close to that of real GOCE data. In the second study, the LMS algorithm is implemented on real GOCE data. Experiments are also performed to determine suitable filtering parameters. Only the four accurate components of the full GOCE gravity gradient tensor of the disturbing potential are used. The characteristics of the filtered gravity gradients are examined in the time and spectral domain. The obtained filtered GOCE gravity gradients show an agreement of 63–84 mEötvös (depending on the gravity gradient component), in terms of RMS error, when compared to the gravity gradients derived from the EGM2008 geopotential model. Spectral-domain analysis of the filtered gradients shows that the adaptive filters slightly suppress frequencies in the bandwidth of approximately 10–30 mHz. The limitations of the adaptive LMS algorithm are also discussed. The tested filtering algorithm can be connected to and employed in the first computational steps of the space-wise approach, where a time-wise Wiener filter is applied at the first stage of GOCE gravity gradient filtering. The results of this work can be extended to using other adaptive filtering algorithms, such as the recursive least-squares and recursive least-squares lattice filters.     

GNSS/声学联合定位的自适应滤波算法

海底控制点布设是构建海洋时空基准的重要环节,而可靠的海洋基准定位模型及方法又是实现高精度海底控制点布设的前提和基础。应用广泛的走航船测量方式兼具灵活性和可控性,但载体异常扰动影响不可避免,易导致海底控制点联合定位模型解算失真。针对这一问题,本文提出一种基于自适应选权滤波的GNSS/声学联合解算方法。首先推导了统一海面及水下观测过程的GNSS/声学联合定位数学模型;然后研究了在联合模型的自适应滤波解算中对载体异常扰动的判别标准,给出了各状态参数自适应因子的构造方法;最后通过仿真和实测数据进行了试验验证。结果表明:引入自适应滤波算法后,能有效改善状态扰动对GNSS/声学联合定位的异常影响,提升其定位稳定性及定位精度;当分别对各类状态参数的自适应因子进行合理构造后,滤波效果可达最佳。     

Soft-Computing Modelling of Seismicity in the Southern Hellenic Arc

This letter investigates the possible coalition of time intervals and patterns in seismic activity during the preparation process of consecutive sizeable seismic events (i.e., $M_{S} geq 5.9$). During periods of low-level seismic activity, stress processes in the crust accumulate energy at the seismogenic area, while larger seismic events act as a decongesting mechanism that releases considerable amounts of that energy. Monthly mean seismicity rates have been introduced as a tool to monitor this energy management system and to divert this information into an adaptive neuro-fuzzy inference system. The purpose of the neuro-fuzzy model is to identify and to simulate the possible relationship between mean seismicity rates and time intervals among consecutive sizeable earthquakes. Successful training of the neuro-fuzzy model results in a real-time online processing mechanism that is capable of estimating the time interval between the latest and the next forthcoming sizeable seismic event.      

基于EMD-自适应滤波的干涉图去噪方法研究

提出了一种基于EMD-自适应滤波干涉图去噪方法,该方法基于信号和噪声经过经验模态分解后在不同的IMF上有不同的特征,即先对信号进行经验模态分解,然后对各个高频IMF信号分别选用不同的滤波梯度参数进行自适应滤波处理,从初始干涉图上减去与斑点噪声所对应尺度信息,从而达到噪声抑制的目的。通过实验对比研究了该算法与Goldstein滤波、圆周期中值滤波、EMD分解方法和梯度-自适应滤波去噪的降噪效果。实验表明,该方法不仅能有效地去除InSAR干涉图的噪声,并且能很好地保持相位的细节信息和条纹的边缘信息。     

GPS码跟踪环自适应多径消除算法

根据多径信号的产生机理,在对GPS接收机中的码跟踪环多径信号模型研究的基础上,提出了采用自适应滤波的来消除GPS多径效应的算法。自适应滤波的方法不需要估计模型的系统参数,而直接通过自适应滤波将多径信号滤除。在有噪声的情况下,自适应滤波的RLS算法是最小二乘意义下的最优估计,仿真的结果表明采用自适应滤波算法可以快速的消除多径的影响,修正鉴相函数的过零点偏差,提高码跟踪环的跟踪精度。由于自适应滤波算法是递推算法,易于软、硬件实现。     

自适应滤波在LEO星载GPS实时定轨中的应用

针对低轨卫星LEO星载GPS实时定轨中存在的问题,提出了以单点定位结果为观测值,采用自适应卡尔曼滤波(AKF)方法进行动力平滑来实现LEO星载GPS实时动力法定轨。采用2004-03-29~31日的GRACE-A卫星实测数据进行了实时定轨计算,并分析了自适应因子、噪声补偿方差、GPS信号中断对自适应定轨的影响。通过计算分析发现,采用AKF进行LEO星载GPS实时定轨可有效解决采用EKF噪声补偿方差难以确定的难题。同时还发现,采用AKF进行LEO星载GPS实时定轨具有较强的稳定性。     

基于FIR滤波的磁静期电离层-磁层磁场信号分离

Swarm是欧空局第4个"地球探测者"任务,其主要目的是确定地球磁场及其时空演化特征。利用Swarm构建地球各圈层磁场模型的关键问题之一是对不同场源的地磁信号实施准确分离。以Swarm磁静期观测数据为例,采用FIR沿轨高通滤波对外源场信号进行处理,分析并给出了滤波器窗口长度的确定方法,利用欧空局2016年发布的MIO_SHA_2D和MMA_SHA_2C电离层-磁层磁场模型验证了该方法的可行性。结果表明:FIR对外源场长波信号的滤波结果与模型改正结果的一致性吻合较好,在给定通带波长1 200 km、阻带波长3 000 km的情况下,将滤波器长度设置为87~129阶,可同时顾及水平方向和径向方向的滤波精度。     

干涉复小波复数域双变量滤波算法

针对复小波双变量滤波模型仅考虑小波复系数实部,忽略了系数的虚部,导致信号相位噪声的增加而影响滤波效果的问题,提出基于复小波变换的复数域双变量模型干涉图滤波算法。该算法将双变量贝叶斯估计算法从实数域推广到了复数域,用噪声复系数概率密度函数刻画了小波复系数实部与虚部的相关性,根据小波分解复系数来估计噪声方差和信号方差,建立了复小波复数域双变量滤波模型,求得了干涉图复系数的贝叶斯估计。试验结果表明,本算法对干涉图噪声有较强的抑制能力,保留了干涉图的边缘及细节信息,滤波性能优于传统的实数域复小波双变量滤波、Goldstein滤波、单小波滤波和最优化融合滤波方法。     

基于模糊理论构造的自适应滤波算法

本文在自适应滤波原理的基础上,结合模糊控制理论,提出了一种基于模糊控制的自适应滤波方法,它是基于滤波处理后的数据残差构造一模糊控制器来自适应控制卡尔曼滤波器的自适应因子α,从而合理调节动力学模型对导航解的贡献。并通过算例验证了该方法的可行性、有效性。     

动力学模型自适应滤波算法研究

在全球导航卫星系统（global navigation satellite system,GNSS）动态测量中,常采用Kalman滤波进行导航解算。但是,载体运动的不规则性经常会导致动力学模型偏差增大,从而出现定位精度下降的问题。针对此,在实时估计协同转弯模型（coordinated turn,CT）转弯率的基础上提出了两种减弱动力学模型偏差影响的自适应滤波算法。一种是实时估计转弯率的CT模型与改进的椭球约束方程相结合的滤波算法;另一种是通过对载体运动规律的分析,推导了实时估计转弯率的三维转弯模型,提出了一种三维转弯模型与新息向量构造的自适应因子相结合的自适应滤波算法。实验结果表明,这两种算法在不同的机动情况下都能较好地控制动力学模型误差的影响,其精度明显优于标准Kalman滤波和CT模型与常速度模型相结合的滤波算法。尤其是第二种算法,不仅通过自适应估计提高了动力学模型的精确性,还通过自适应因子进一步控制了动力学模型扰动的影响,显著提高了动态导航解的精度和可靠性。     

一种抑制GPS射频干扰的空频自适应处理算法

为了提高窄带干扰与期望卫星信号同向时的抑制性能,提出了一种新的空频自适应滤波算法。该算法首先利用频域实系数自适应滤波对信号进行预处理抑制窄带干扰,然后在频域直接对处理后的信号进行空频自适应处理抑制宽带干扰。算法在预处理时采用自适应滤波可有效减少期望卫星信号的损失以及SFAP导向矢量误差。仿真实验表明,相对于普通的空时及空频自适应处理算法,新算法有效地提高了输出信干噪比,在干扰与信号同向的情况下也能表现出优良的处理性能。     

A locally adaptive filter of interferometric phase images

We propose an adaptive filtering approach for interferograms, which is a modification to the Lee adaptive complex filter. Based on local frequency estimates, we compute the normal orientation of local phase fringes. A directionally dependent filtering window is aligned perpendicular to the normal orientation of local phase fringes (i.e., along local phase fringes) by interpolation, making the pixels included in the filtering window have approximately more homogeneous values. Moreover, the computation of the filter parameter does not require local phase unwrapping in the real plane. This filter minimizes the loss of signal and reduces the level of noise. By using two sets of simulated data, its effectiveness can be seen in terms of the fidelity to noise-free phases, fringe preservation, and residue reduction.     

支持向量回归辅助的GPS/INS组合导航抗差自适应算法

卡尔曼滤波残差分量受到观测信息误差和动力学模型误差的双重影响,由于GPS/INS松耦合导航系统中观测值个数少于状态参数个数,导致异常检测时难以正确区分误差来源,提出一种支持向量回归辅助的组合导航抗差自适应算法。该算法克服了组合系统观测信息无冗余情况下异常检测的局限性,基于遗传算法参数寻优构建回归模型,预测次优观测值,结合整体异常检验法自主选择抗差或自适应滤波,进而调整观测值或动力学模型对导航解的贡献,进行导航预报。最后利用车载实测数据进行验证,结果表明：该算法能够对存在的异常故障智能判定,减弱观测值异常和动力学模型误差影响,保证组合导航精度,提高导航解可靠性。     

一种改进均值的自适应中值滤波算法

针对自适应中值滤波算法在滤除高浓度椒盐噪声和保留图像边缘细节中的不足,提出了一种改进均值的自适应中值滤波(IMAMF)算法。该算法采用扩充图像边界的方式,使得原图像的边界点能在自适应的滤波窗口下参与噪声检测和滤波处理,并在检测噪声和信号时,增加了噪声阈值判定,将存在噪声的像素点用修正后的均值滤波器值输出,信号点则用原始灰度值输出。为了验证算法的可行性,采用了5种不同的算法进行仿真对比分析,并从主观角度和客观指标上进行效果评价。试验结果表明:该算法能有效滤除浓度为10%～90%范围内的椒盐噪声,且图像细节和边缘信息得到了更好的保留,滤波性能明显优于其他算法。     

结合KSTEST统计检验方法的联合像素时序干涉SAR处理技术

联合像素干涉SAR处理技术属于第二代时序干涉SAR技术,技术流程分为3步：联合像素信号模型建立,拟合优度检验,空域自适应滤波。本文在一维KSTEST统计检验方法基础上,提出了基于多维联合像素的非参数拟合优度检验方法,利用实测数据将该方法与传统的多时相干涉SAR技术结果对比,使形变监测点空间密度得到有效提升,形变监测能力得到进一步提高。     

改进的Sage—Husa自适应滤波在GPS动态导航定位中的应用

本文利用Kalman滤波方法对动态测量进行数据处理,由于高动态的GPS测量,不易确定系统动态噪声和观测噪声.同时标准的Kalman滤波在应用过程中由于状态模型确定的误差存在,滤波效果不佳.因此本文结合动态导航的实时性和高动态性,建立了动态导航系统中滤波状态方程和观测方程,采用改进的Sage-Husa自适应滤波对来进行实时定位数据处理,利用已有测量数据进行了实例分析.改进的Sage-Husa自适应滤波在计算过程中计算量小,结果稳定,有较强的自适应性.     

基于自适应噪声抵消与小波滤波的GPS监测误差分析

对GPS载波相位测量误差进行了理论分析和试验研究。根据噪声特征以及它们对载波相位测量结果的影响，提出了基于自适应噪声抵消与小波滤波相结合的GPS噪声抑止方法。对具有强相关特性的多路径误差采用自适应噪声抵消方法，而对于不相关的高频噪声则通过合理选择小波分解层数对信号进行分解，对作用闽值后的小波系数进行重构，得到最后的变形信号。实测数据分析表明，该方法能有效地削弱多路径效应及测量随机噪声，较用单一方法对GPS数据进行消噪处理有较大的优越性。     

Heterogeneous surface displacement pattern at the Hatchobaru geothermal field inferred from SAR interferometry time-series

We estimated surface displacements using persistent scatterer SAR interferometry (PS-InSAR) around the Hatchobaru geothermal field, Japan, from 18 ALOS/PALSAR images acquired from July 2007 to December 2010. Generally, geothermal fields, covered with natural targets such as rocky terrain and vegetation, have been one of the difficult targets for PS-InSAR analysis. However, we applied space adaptive filtering to increase the number of pixels for measuring surface displacement. The results of our analysis demonstrate ground subsidence with decaying velocity over the observation period around the geothermal field. The spatial pattern of ground subsidence includes sharp boundaries of subsidence that can be interpreted as fault traces. We demonstrated the usefulness of PS-InSAR analysis with the space adaptive filtering to estimate surface displacements with high spatial resolution and high spatial density around a geothermal field.     

自适应卡尔曼滤波在大跨径桥梁安全监控中的应用

对由卡尔曼滤波理论建立的数据模型进行了研究,建立了自适应动态滤波模型,并用该模型对南京长江某大跨径桥梁的实际监测资料进行了分析,结果表明该模型能正确反映桥梁变化.     

基于自适应 Kalman 滤波的边坡变形预测研究

研究Kalman滤波和自适应Kalman滤波算法,结合边坡监测点的运动模型将其应用于边坡变形监测动态数据变形预测。利用小湾水电站二号山梁高边坡GPS监测数据进行实验研究。结果表明,自适应Kalman滤波在边坡三维形变预测及变形速率估算方面有很好的预测结果。