A new maximum-likelihood phase estimation method for X-ray pulsar signals

X-ray pulsar navigation （XPNAV） is an attractive method for autonomous navigation of deep space in the future. Currently, techniques for estimating the phase of X-ray pulsar radiation involve the maximization of the general non-convex object functions based on the average profile fxom the epoch folding method. This results in the suppression of useful information and highly complex computation. In this paper, a new maximum likelihood （ML） phase estimation method that directly utilizes the measured time of arrivals （TOAs） is presented. The X-ray pulsar radiation will be treated as a cyclo-stationary process and the TOAs of the photons in a period will be redefined as a new process, whose probability distribution function is the normalized standard profile of the pulsar. We demonstrate that the new process is equivalent to the generally used Poisson model. Then, the phase estimation problem is recast as a cyclic shift parameter estimation under the ML estimation, and we also put forward a parallel ML estimation method to improve the ML solution. Numerical simulation results show that the estimator described here presents a higher precision and reduces the computational complexity compared with currently used estimators.     

An approximate method of state estimation for non-linear dynamical systems with state-dependent noise†

In this paper a method of stochastic linearization is demonstrated for the purpose of establishing an approximate approach to solve filtering problems of non-linear stochastic systems with state-dependent noise in a Markovian framework.

The models of both the dynamical system and the observation process are described by non-linear stochastic differential equations of Itô type.

The principal line of attack is to expand the non-linear drift term into a certain linear function with coefficients which are determined under the minimal squared error criterion. Two methods of linearization are developed for the non-linear diffusion term. The linearized models are thus characterized by expansion coefficients dependent on both the state estimate and the error covariance.

A method is given for the simultaneous treatment of the approximate structure of state estimator dynamics and of the running evaluation of the error covariance, including quantitative aspects of sample path behaviours obtained by digital simulation studies.     

An efficient semantic – Related image retrieval method

Many previous techniques were designed to retrieve semantic images in a certain neighborhood of the query image and thus bypassing the semantically related images in the whole feature space. Several recently methods were designed to retrieve semantically related images in the entire feature space but with low precision. In this paper, we propose a Semantic – Related Image Retrieval method (SRIR), which can retrieve semantic images spread in the entire feature space with high precision. Our method takes advantage of the user feedback to determine the semantic importance of each query and the importance of each feature. In addition, the retrieval time of our method does not increase with the number of user feedback. We also provide experimental results to demonstrate the effectiveness of our method.     

An image geometry model for METEOSAT†

METEOSAT is a spin-stabilized geostationary satellite which takes Earth images in three spectral channels, infrared, visible and water vapour absorption band. The time-dependent deviations of the satellite from nominal position, attitude and spin speed are described by an image geometry model. A new iterative procedure for refined attitude determination is used. The attitude is derived from northern and southern polar horizon scan limits which are extracted from infrared image data. The geometry model provides a deformation vector field which relates the ideal reference image to the actual image. The actual image is then mapped to the reference image by the nearest-neighbour rectification process.     

An image segmentation method based on Mumford–Shah model with mask factor and neighborhood factor

Pattern Analysis and Applications - A novel image segmentation model is proposed to improve the stability of existing segmentation methods. In the proposed model, we introduce two factors into the...     

An adaptive enhancement method for generic multi-modal high‐bit RAW grayscale image displaying on low-bit monitors

Aiming at the poor adaptability and robustness of existing pseudo-color methods, most of which can only deal with an 8-bit grayscale, an adaptive enhancement algorithm of high grayscale images based on priori knowledge was proposed. Firstly, aiming at the problem that the original RGB color space is not easy to adjust dynamically, the power adjustment was integrated into the constructed high-bit chromatogram, and a power adaptive adjustment function based on the brightness priority over the original grayscale image was designed. Secondly, aiming at the problems of over-exposure, under-exposure, and poor gradients in RGB space, an adaptive grayscale correction algorithm was designed according to the priori knowledge distribution of RGB perceived brightness. Finally, to guarantee the color balance of the enhanced image, a color balance correction algorithm based on CMY space was designed. To verify the effectiveness of this method, it was applied to the pseudo-color enhancement of 16-bit pipeline CR images, 14-bit infrared images, 24-bit pipeline DR weld images, 8-bit or 24-bit rail crack images, and 16-bit remote sensing images respectively. The subjective and objective experimental results show that the design method has stronger adaptability, which has obvious advantages compared with the existing advanced high grayscale image enhancement methods. The enhancement effect is more coordinated, the processing result is more in line with human visual perception, and the details and texture information of the original image can be better preserved.     

The characteristic-Galerkin method for advection-dominated problems—An assessment

In this note we present results of an accuracy analysis of a recent characteristic-based Galerkin method suited for advection-dominated problems. The analysis shows that the numerical propagation characteristics of the explicit time-stepping scheme which uses linear basis functions for spatial discretization are superior to those of the related classical Lax-Wendroff method and the implicit Crank-Nicolson scheme. The model is subjected to three analytical test problems which embrace many essential realistic features of environmental and coastal hydrodynamic applications: pure advection of a steep Gaussian profile, dispersion of a continuous source in an oscillating flow, and long-wave propagation with bottom frictional dissipation in a rectangular channel. The numerical results demonstrate that the accuracy achieved with the present scheme is excellent and comparable to that of a characteristic-based finite difference scheme which uses Hermitian cubic interpolating polynomials. The results reported herein suggest strongly further use and testing of this robust model in engineering practice.     

A combined DEM–FEM numerical method for Shot Peening parameter optimisation

A numerical modelling approach capable of simulating Shot Peening (SP) processes of industrial interest was developed by combining the Discrete Element Method (DEM) with the Finite Element Method (FEM).In this approach, shot–shot and shot–target interactions as well as the overall shot flow were simulated efficiently using rigid body dynamics. A new algorithm to dynamically adapt the coefficient of restitution (CoR) for repeated impacts of shots on the same spot was implemented in the DEM code to take into account the effect of material hardening. Then, a parametric study was conducted using the Finite Element Method (FEM) to investigate the influence of the SP parameters on the development of residual stresses.Finally, a two-step coupling method is presented to combine the output of DEM simulation with FEM analyses to retrieve the Compressive Residual Stresses (CRS) after multiple impacts with the aim to evaluate the minimum area required to be modelled to realistically capture the field of residual stresses. A series of such coupled analyses were performed to determine the effect of peening angle and the combination of initial velocity and mass flow rate on CRS.     

Reduced-order H_∞ filtering for navigation with carrier phase

A reduced-order H∞filtering algorithm for navigation with carrier phase is presented.By taking advantage of decoupling between filtering states in a navigation solution,this algorithm reduces computational cost and is robust in colored noise.Furthermore,the estimation precision is also improved by taking ambiguities as nuisance states when the filtering process converges.Applications to kinetic simulations under diferent noise are presented to demonstrate robustness and efciency of the algorithm.     

基于加权联合导向矢量模型的InSAR干涉相位估计

基于干涉图的传统干涉相位估计方法,当由于图像配准误差而导致的干涉图质量较差时,就难以恢复出准确的真实干涉相位.本文提出了一种基于加权联合导向矢量模型的InSAR干涉相位估计方法.该方法构造最优联合观测矢量和加权联合导向矢量,同时利用相邻像素的相干信息,并采用波束形成技术,因此具有自适应图像配准和降低相位噪声的功能,因而可以在SAR图像配准精度很差(可以允许达到一个分辨单元)的条件下准确地估计相应像素间的干涉相位.仿真及实测数据的处理结果证明了此方法的有效性.     

InDeandCoE: A framework based on multi-scale feature fusion and residual learning for interferometric SAR remote sensing image denoising and coherence estimation

Synthetic aperture radar (SAR) interferometry is a high-resolution microwave remote sensing imaging method. Over the past two decades, many researchers working on remote sensing have applied this technology in various disciplines, including environmental monitoring, disaster monitoring, and elevation mapping. However, due to the existence of many influencing factors in the acquisition stage, such as atmospheric humidity and temperature, the reflected wave signals from the ground will be disturbed when received by remote sensing satellites. The presence of noise in interferograms is inevitable. Therefore, the accuracy of interferometric SAR phase denoising and coherence estimation has a decisive impact on the validity of subsequent processing results. In this paper, we pioneer the use of a nested U-net as a feature extractor for interferometric SAR phase and coherence. In addition, we build a phase filter and a coherence estimator by using the residual learning module. With the aim of determining the unique non-local similarity of InSAR images, we use non-local convolution and channel attention mechanisms to extract features in different dimensions of the interferogram. Through quantitative and qualitative experiments, the proposed method performs better in phase denoising and coherence estimation than state-of-the-art methods.     

快速区域互相关InSAR图像配准方法

在分析复图像干涉相位对快速互相关算法配准精度影响的基础上,提出了一种快速区域互相关InSAR图像配准方法。该方法对图像的幅度谱进行区域相关操作,获得干涉相位空间角频率的粗估计,并对主图像进行干涉相位补偿。该算法通过上述步骤消除干涉相位变化对算法的影响实现了高精度配准。在仿真实验和实测实验中,通过与传统的快速区域互相关算法及最大频谱法进行对比,验证了本文算法的稳健性与有效性。     

基于信号子空间估计的多基线InSAR干涉相位图滤波方法

多基线干涉合成孔径雷达（Interferometic synthetic aperture radar,InSAR）利用长短基线之间的关系,能够获得优于单基线InSAR的高程测量结果。本文针对多基线InSAR的数据特点,提出了基于信号子空间估计的多基线InSAR干涉相位图滤波方法。该方法将不同长度基线下所获得干涉相位图中同一像素单元信号作为一个训练样本,通过信号子空间的估计完成多基线InSAR干涉相位图滤波。仿真实验结果表明,本文方法可以在运算时间相当的情况下,获得优于回转均值滤波算法和回转中值滤波算法的滤波性能,是一种可满足实时处理要求的有效的多基线InSAR干涉相位图滤波方法。     

基于残差点的干涉相位图自适应滤波

为了能有效地对InSAR干涉相位图进行滤波,利用残差点矩阵作为滤波控制因子,对两种干涉相位图滤波方法进行了改进。该措施在不破坏致密的干涉条纹的情况下,通过引用干涉相位图的残差矩阵,对不同区域的图像采用不同强度的滤波器来实现自适应的降噪,同时详细描述了滤波方法的实施步骤,并用ERS-1重复轨道数据进行了验证,结果表明,该改进方法可取得良好的滤波效果。     

多基线InSAR干涉相位的自适应估计*

针对不均衡噪声下相位解缠稳健性低的缺点,采用加权联合数据矢量作为信号模型,利用构造的噪声协方差矩阵进行自适应去噪处理,进而对相位进行估计,有效地改善了不均衡噪声下多基线相位估计的效果。仿真数据的处理结果表明,该算法提高了不均衡噪声下相位估计的稳健性。     

结合全局上下文与融合注意力的干涉相位去噪

目的 干涉相位去噪是合成孔径雷达干涉测量（interferometric synthetic aperture radar,InSAR）技术中的关键环节,其效果对测量精度具有重要影响。针对现有的干涉相位去噪方法大多关注局部特征以及在特征提取方面的局限性,同时为了平衡去噪和结构保持两者之间的关系,提出了一种结合全局上下文与融合注意力的相位去噪网络GCFA-PDNet(global context and fused attention phase denoising network)。方法 将干涉相位分离为实部和虚部依次输入到网络,先从噪声相位中提取浅层特征,再将其映射到由全局上下文提取模块和融合注意力模块组成的特征增强模块,最后通过全局残差学习生成去噪图像。全局上下文提取模块能提取全局上下文信息,具有非局部方法的优势;融合注意力模块既强调关键特征,又能高效提取隐藏在复杂背景中的噪声信息。结果 所提出的方法与对比方法中性能最优者相比,在模拟数据结果的平均峰值信噪比（peak signal to noise ratio, PSNR）和结构相似性（structural similarity,...     

Multi-temporal monitoring of wetland water levels in the Florida Everglades using interferometric synthetic aperture radar (InSAR)

Interferometric synthetic aperture radar (InSAR) techniques can successfully detect phase variations related to the water level changes in wetlands and produce spatially detailed high-resolution maps of water level changes. Despite the vast details, the usefulness of the wetland InSAR observations is rather limited, because hydrologists and water resources managers need information on absolute water level values and not on relative water level changes. We present an InSAR technique called Small Temporal Baseline Subset (STBAS) for monitoring absolute water level time series using radar interferograms acquired successively over wetlands. The method uses stage (water level) observation for calibrating the relative InSAR observations and tying them to the stage's vertical datum. We tested the STBAS technique with two-year long Radarsat-1 data acquired during 2006-2008 over the Water Conservation Area 1 (WCA1) in the Everglades wetlands, south Florida (USA). The InSAR-derived water level data were calibrated using 13 stage stations located in the study area to generate 28 successive high spatial resolution maps (50 m pixel resolution) of absolute water levels. We evaluate the quality of the STBAS technique using a root mean square error (RMSE) criterion of the difference between InSAR observations and stage measurements. The average RMSE is 6.6 cm, which provides an uncertainty estimation of the STBAS technique to monitor absolute water levels. About half of the uncertainties are attributed to the accuracy of the InSAR technique to detect relative water levels. The other half reflects uncertainties derived from tying the relative levels to the stage stations' datum.     

SAR图像目标方位角联合估计方法研究

为了精确估计任意角度的目标方位角,融合主导边界法与峰值拟合法,提出一种SAR目标方位角的联合估计方法。通过计算投影比以及目标区域同外接矩形面积之比,初步判断目标是否位于水平或垂直方位附近,自适应选择相应的估计算法进行处理。移动与静止目标获取与识别(MSTAR)公共数据库实测数据的仿真结果表明,该方法估计误差较小,可获得较高的估计准确度。方位角联合估计方法有利于增强目标方位角估计的适应性,提高目标分类和识别的效率。