A note on observation decorrelation,variances of residuals,and redundancy numbers

A technique for processing correlated observations suitable for large, sparse, least-squares adjustments is reviewed. Correlated coordinate differences derived from the Global Positioning System are used as illustrative examples. However, the methods examined are suitable for all types of correlated observations. The computation of variances of residuals, redundancy numbers, and marginally detectable errors is considered for sparse systems.     

一种相关观测的Partial EIV模型求解方法

Partial errors-in-variables(Partial-EIV)模型作为EIV模型的扩展形式,其构造方式更有规律,解算方法更为简便,能有效应用于实际情况。针对已有Partial EIV模型方法未考虑观测向量和系数矩阵存在相关性这一情况,通过提取观测向量和系数矩阵组成的增广矩阵中非重复出现的随机元素,构建更具一般适用性的Partial EIV模型,在该模型的基础上,将特殊假定条件扩展到不限定观测数据相关性的一般情况,详细推导了观测向量和系数矩阵元素相关且不等精度情况下的加权总体最小二乘方法,通过算例试验,并与目前已有的解决EIV模型相关观测情况下的方法进行了比较分析,研究表明本文方法可以提高计算效率,更具一般性,特别是对于观测向量和系数矩阵中存在常数元素和重复元素的情况。     

论观测值的可靠性度量

国内外许多文献采用观测值的多余观测分量定义可靠性指标，并且认为多余观测分量的值域为区间［０，１］。本文的研究表明，对于相关值而言，上述结论不一定成立，本文借助于一般平差模型，考虑了观测值之间可能存在的相关性，导出了具有普遍意义的观测值可靠性度量指标，深入讨论了多余观测分量与可靠性指标的关系，作为可靠性指标的应用例子，讨论了ＧＰＳ相位三次差分法探测周跳的能力。     

Sparse representation-based correlation analysis of non-stationary spatiotemporal big data

As the basic data of digital city and smart city research, Spatiotemporal series data contain rich geographic information. Alongside the accumulation of spatial time-series data, we are also encountering new challenges related to analyzing and mining the correlations among the data. Because the traditional methods of analysis also have their own suitable condition restrictions for the new features, we propose a new analytical framework based on sparse representation to describe the time, space, and spatial-time correlation. First, before analyzing the correlation, we discuss sparse representation based on the K-singular value decomposition (K-SVD) algorithm to ensure that the sparse coefficients are in the same sparse domain. We then present new computing methods to calculate the time, spatial, and spatial-time correlation coefficients in the sparse domain; we then discuss the functions' properties. Finally, we discuss change regulations for the gross domestic product (GDP), population, and Normalized Difference Vegetation Index (NDVI) spatial time-series data in China's Jing-Jin-Ji region to confirm the effectiveness and adaptability of the new methods.     

A map-matching algorithm dealing with sparse cellular fingerprint observations

ABSTRACT

The widespread availability of mobile communication makes mobile devices a resource for the collection of data about mobile infrastructures and user mobility. In these contexts, the problem of reconstructing the most likely trajectory of a device on the road network on the basis of the sequence of observed locations (map-matching problem) turns out to be particularly relevant. Different contributions have demonstrated that the reconstruction of the trajectory of a device with good accuracy is technically feasible even when only a sparse set of GNSS positions is available. In this paper, we face the problem of coping with sparse sequences of cellular fingerprints. Compared to GNSS positions, cellular fingerprints provide coarser spatial information, but they work even when a device is missing GNSS positions or is operating in an energy saving mode. We devise a new map-matching algorithm, that exploits the well-known Hidden Markov Model and Random Forests to successfully deal with noisy and sparse cellular observations. The performance of the proposed solution has been tested over a medium-sized Italian city urban environment by varying both the sampling of the observations and the density of the fingerprint map as well as by including some GPS positions into the sequence of fingerprint observations.     

Impacts of BeiDou stochastic model on reliability: overall test,w-test and minimal detectable bias

GPS Solutions - Extensive studies have concluded that the GNSS observations are heteroscedastic and physically correlated. Typically, the observation precisions are elevation dependent and...     

关联向量机在高光谱影像分类中的应用

将关联向量机应用于高光谱影像分类, 实现高维空间中训练样本不足时分类器的精确建模。从稀疏贝叶 斯理论出发, 分析关联向量机原理, 探讨一对多、一对一和两种直接的多分类方法。实验环节比较了各种多分类方 法, 并从精度、稀疏性两方面将关联向量机与支持向量机等经典算法比较。实验结果表明, 两种直接的多分类方法 内存占用大、效率低; 一对多精度最高, 但效率较低; 一对一计算效率最高, 精度与一对多近似。关联向量机精度 不如支持向量机, 但解更稀疏, 测试样本较多时实时性好, 适合处理大场景高光谱影像的分类问题。     

基于地面观测资料的MODIS云量产品订正

通过对MODIS云量数字产品与气象站云量观测资料的对比分析,发现两者间存在较大偏差。本文以气象站观测云量资料为基准,提出了差值订正、比值订正、差值混合订正、比值混合订正和归一化混合订正5种MODIS云量数字产品的订正方法。结果表明:5种订正方法均有效,其中比值订正法最简单易行,且效果最好,是基于地面观测资料MODIS云量数字产品的最优订正方法。订正后的MODIS云量与气象站观测云量在空间分布特征和数值上都非常吻合。加密站验证结果表明:各月绝对误差平均值均小于5%。本研究为利用地面观测资料订正相关卫星数字产品提供了借鉴方法,有效发挥了卫星空间连续观测的优势,对高原、荒漠和山地等地面测站稀缺地区的相关研究具有重要意义。     

大地测量相关观测抗差估计理论

相关观测异常诊断、质量控制是测量数据处理领域亟待解决的难题之一。分别从方差膨胀模型和相关权元素压缩模型入手研究了相关观测的质量控制理论和方法；给出了误差影响函数；构造了方差膨胀函数和权因子收缩函数；利用观测量的等价协方差阵和等价权矩阵讨论了相关观测质量控制的计算方法。该等价协方差矩阵和等价权矩阵不仅保持了原有协方差矩阵和权矩阵的对称性，而且保持了原有协方差矩阵的相关性不变。计算结果表明异常观测的方差膨胀法和等价权法能有效地控制异常观测对参数估值的影响。     

A computationally efficient approach for estimating high-rate satellite clock corrections in realtime

Realtime satellite clock corrections are usually estimated using undifferenced phase and range observations from a global network. Because a large number of ambiguity parameters must be estimated, the computation is time-consuming. Consequently, only a sparse global network of limited number of stations is processed by most IGS Realtime Analysis Centers with an update rate of 5 s. In addition, it is very desirable to build the capability to simultaneously estimate clock corrections for multi-GNSS constellations. Although the estimation can be sped up by epoch-differenced observations that eliminate ambiguities, the derived clocks can contain a satellite-specific bias that diminishes the contribution of range observations. We introduce a computationally efficient approach for realtime clock estimation. Both the epoch-differenced phase and undifferenced range observations are used together to estimate the epoch-differenced satellite clocks and the initial clock bias for each satellite and receiver. The biased clock corrections accumulated from the estimated epoch-differenced clocks are then aligned with the estimated clock biases and provided as the final clock corrections to users. The algorithm is incorporated into the EPOS-RT software developed at GFZ (GeoForschungsZentrum) and experimentally validated with the IGS global network. The comparison with the GFZ rapid products shows that the accuracy of the clock estimation with the new approach is comparable with that of the undifferenced approach, whereas the computation time is reduced to one-tenth. As a result, estimation of high-rate satellite clocks from a large reference network and tracking satellites of multi-GNSS constellations becomes achievable.     

过完备字典稀疏表示的云图超分辨率算法

提出一种基于过完备字典稀疏表示的云图超分辨率算法。首先,联合训练针对低分辨率与高分辨率云图块的两个字典Dl和Dh,保证对应的低分辨率与高分辨率云图块关于各自的字典具有相似的稀疏表示;其次,通过求解优化问题,获得待处理云图每个低分辨率云图块关于Dl的稀疏表示,并将表示系数用于Dh以生成对应的高分辨率云图块;最后,运用最速下降算法,得到满足重构约束的高分辨率云图。红外与可见光云图的数值实验验证了本文算法的有效性,表明本文算法在视觉效果及PSNR指标上均优于插值方法。     

CATS: GPS coordinate time series analysis software

Over the last 10 years, several papers have established that daily estimates of GPS coordinates are temporally correlated and it is therefore incorrect to assume that the observations are independent when estimating parameters from them. A direct consequence of this assumption is the over-optimistic estimation of the parameter uncertainties. Perhaps the perceived computational burden or the lack of suitable software for time series analysis has resulted in many heuristic methods being proposed in the scientific literature for estimating these uncertainties. We present a standalone C program, CATS, developed to study and compare stochastic noise processes in continuous GPS coordinate time series and, as a consequence, assign realistic uncertainties to parameters derived from them. The name originally stood for Create and Analyze Time Series. Although the name has survived, the creation aspect of the software has, after several versions, been abandoned. The implementation of the method is briefly described to aid understanding and an example of typical input, usage, output and the available stochastic noise models are given.     

An approach to response-based reliability analysis of quasi-linear Errors-in-Variables models

The paper presents an approach to internal reliability analysis of observation systems known as Errors-in-Variables (EIV) models with parameters estimated by the method of least squares. Such problems are routinely treated by total least squares adjustment, or orthogonal regression. To create a suitable environment for derivations in the analysis, a general nonlinear form of such EIV models is assumed, based on a traditional adjustment method of condition equations with unknowns, also known as the Gauss–Helmert model. However, in order to apply the method of reliability analysis based on the approach to response assessment in systems with correlated observations, presented in the earlier work of this author, it was necessary to confine the considerations to a quasi-linear form of the Gauss–Helmert model, representing quasi-linear EIV models. This made it possible to obtain a linear disturbance/response relationship needed in that approach. Several specific cases of quasi-linear EIV models are discussed. The derived formulas are consistent with those already functioning for standard least squares adjustment problems. The analysis shows that, as could be expected, the average level of response-based reliability for such EIV models under investigation is lower than that for the corresponding standard linear models. For EIV models with homoscedastic and uncorrelated observations, the relationship between the average reliability indices for the independent and the dependent variables is formulated for multiple regression and coordinate transformations. Numerical examples for these two applications are provided to illustrate this analysis.     

加速迭代硬阈值ISAR稀疏成像

加速迭代硬阈值(AIHT)作为一种压缩感知(CS)算法,具有计算复杂度低、内存占用小的特点,在较小数目的观测下,经过有限次迭代便能获得较好的重建效果。因此,本文将该算法应用到逆合成孔径雷达(ISAR)稀疏成像,对该方法的具体特征和适用性进行了详细分析。仿真结果表明,在相同信噪比和回波脉冲数条件下,基于AIHT的ISAR成像方法,相比于稀疏贝叶斯算法(SBL),在重建时间上缩短了至少2个量级;相比于迭代加权最小二乘法(IRLS)和平滑l0范数算法,在重建质量上提高了约3倍。经实测数据处理结果验证,在1/4原始脉冲数条件下,该方法仍具有良好的聚焦性能和压低旁瓣效果。     

黑河流域遥感物候产品验证与分析

植被物候遥感产品对全球变化响应、农业生产管理、生态学的应用等多领域研究具有重要意义。但现有植被物候遥感产品还有较多问题,主要包括一方面使用不同参数的时间序列数据以及不同提取算法导致的产品结果差异较大,另一方面在地面验证中地面观测数据与遥感反演数据的物理含义不一致导致的验证方法的系统性误差。本文以黑河流域为研究区,对比验证基于EVI(Enhanced Vegetation Index)时间序列数据提取的MLCD(MODIS global land cover dynamics product)植被遥感物候产品和基于LAI(Leaf Area Index)时间序列数据提取的UMPM(product by universal multi-life-cycle phenology monitoring method)植被遥感物候产品的有效性及精度等。同时,通过验证分析进一步评估基于EVI和LAI时间序列提取的物候特征的差异及特点,探讨由于地面观测植被物候与遥感提取植被物候的物理意义的不一致问题导致的直接验证结果偏差。结果表明:UMPM产品有效性整体高于MLCD产品,但在以草地和灌木为主的稀疏植被区,由于LAI取值精度的原因,UMPM产品存在较多缺失数据,且时空稳定性较低;基于玉米地面观测数据表明,EVI对植被开始生长的信号比LAI更加敏感,更适合提取生长起点,但植被指数易饱和,峰值起点普遍提前,基于LAI提取的峰值起点更加合理。由于地面观测的物候期在后期更加关注果实生长,遥感观测仅关注叶片的生长,遥感定义的峰值终点和生长终点与玉米的乳熟期和成熟期差异较大。     

Reduced-complexity fusion of multiscale topography and bathymetry data over the Florida coast

The multiscale Kalman smoother (MKS) is a globally optimal estimator for fusing remotely sensed data. The MKS algorithm can be readily parallelized because it operates on a Markov tree data structure. However, such an implementation requires a large amount of memory to store the parameters and estimates at each scale in the tree. This becomes particularly problematic in applications where the observations have very different resolutions and the finest scale data are sparse or aggregated. Such cases commonly arise when fusing data to capture both regional and local structure. In this work, we develop a reduced-complexity MKS algorithm and apply it to the fusion of topographic and bathymetric elevations on the Florida coast.     

Combined GPS/GLONASS data processing

To obtain the GLONASS satellite position at an epoch other than reference time,the satellite‘s equation of motion has to be integrated with broadcasting ephemerides.The iterative detecting and repairing method of cycle slips based on triple difference residuals for combined GPS/GLONASS positioning and the iterative ambiguity resolution approach suitable for combined post processing positioning are discussed systematically.Experiments show that millimter accuracy can be achieved in short baselines with a few hours‘ dual frequency or even single frequency GPS/GLONASS carrier phase observation,and the precision of dual frequency observations is distinctly higher than that of single frequency observations.     

Geology,geomorphology and mineral resources of khodana and adjoining areas (Haryana) and their significance in landuse planning

The area around Khodana, Bhiwani district, Haryana forms a part of the semi-arid region of western India. As vegetation is sparse and mainly xerophytic, photointerpretation technology was effectively utilised in bringing out the broad geology and structural pattern of the mature Precambrian terrain, which were ground-checked and correlated with the results of detailed integrated surveys carried out for base metal sulphide mineralisation in the area. A number of shear/fault zones, sub-parallel to the formational contacts and the regional fold axes, could be recognised in aerial photographs and on ground checking, were found to be intensely limonitised. The possibility of direct application of the pyrite-pyrthotite, encountered in drilling through shear zones, in landuse planning has been discussed. Discrete use of the quartzites as road ballasts, building stone and bund material, use of clay-rich pockets within pasture lands or micro-depressions in the surrounding terrain for locally-constructed brick kilns and ochreous material as pigments are some other uses of finite land resources of Haryana State.     

Structured Sparse Method for Hyperspectral Unmixing

Hyperspectral Unmixing (HU) has received increasing attention in the past decades due to its ability of unveiling information latent in hyperspectral data. Unfortunately, most existing methods fail to take advantage of the spatial information in data. To overcome this limitation, we propose a Structured Sparse regularized Nonnegative Matrix Factorization (SS-NMF) method based on the following two aspects. First, we incorporate a graph Laplacian to encode the manifold structures embedded in the hyperspectral data space. In this way, the highly similar neighboring pixels can be grouped together. Second, the lasso penalty is employed in SS-NMF for the fact that pixels in the same manifold structure are sparsely mixed by a common set of relevant bases. These two factors act as a new structured sparse constraint. With this constraint, our method can learn a compact space, where highly similar pixels are grouped to share correlated sparse representations. Experiments on real hyperspectral data sets with different noise levels demonstrate that our method outperforms the state-of-the-art methods significantly.     

多余观测分量与可靠性度量指标研究

讨论了相关观测值与非相关观测值的多余观测分量值域问题；论证了相关观测值的多余观测分量与内可靠性指标的不一致性；进一步研究了可靠性指标的应用问题，认为当观测值的复相关系数不同时，应同时考虑内、外可靠性指标；最后，以ＧＰＳ准动态测量为例作了分析