Indoor localization for pedestrians with real-time capability using multi-sensor smartphones

ABSTRACT

The localization of persons or objects usually refers to a position determined in a spatial reference system. Outdoors, this is usually accomplished with Global Navigation Satellite Systems (GNSS). However, the automatic positioning of people in GNSS-free environments, especially inside of buildings (indoors) poses a huge challenge. Indoors, satellite signals are attenuated, shielded or reflected by building components (e.g. walls or ceilings). For selected applications, the automatic indoor positioning is possible based on different technologies (e.g. WiFi, RFID, or UWB). However, a standard solution is still not available. Many indoor positioning systems are only suitable for specific applications or are deployed under certain conditions, e.g. additional infrastructures or sensor technologies. Smartphones, as popular cost-effective multi-sensor systems, is a promising indoor localization platform for the mass-market and is increasingly coming into focus. Today’s devices are equipped with a variety of sensors that can be used for indoor positioning. In this contribution, an approach to smartphone-based pedestrian indoor localization is presented. The novelty of this approach refers to a holistic, real-time pedestrian localization inside of buildings based on multi-sensor smartphones and easy-to-install local positioning systems. For this purpose, the barometric altitude is estimated in order to derive the floor on which the user is located. The 2D position is determined subsequently using the principle of pedestrian dead reckoning based on user's movements extracted from the smartphone sensors. In order to minimize the strong error accumulation in the localization caused by various sensor errors, additional information is integrated into the position estimation. The building model is used to identify permissible (e.g. rooms, passageways) and impermissible (e.g. walls) building areas for the pedestrian. Several technologies contributing to higher precision and robustness are also included. For the fusion of different linear and non-linear data, an advanced algorithm based on the Sequential Monte Carlo method is presented.     

略论珠穆朗玛峰重力值的推估

仅根据邻近点的重力与高程资料,采用了4种有关公式,有效地推估了珠穆朗玛峰顶上的重力值,该值为(976970±7)×10^-5m·s^-2。这一结果为精确推求珠峰大地水准面和正高提供了必要的数据     

河口表层悬浮泥沙气象卫星遥感定量模式研究

分析了HRIS光谱图像帧序列的相关性 ,并针对这一特性 ,提出了利用D2 PCM方法 ,降低光谱图像序列的谱相关和空间相关 ,减少图像中的冗余 ,从而实现光谱图像的压缩。该方法提高了压缩比和压缩效率 ,算法运算量小 ,快速。     

定位参数抗差估计的无偏性

根据抗差估计中误差的最基本假设—对称分布 ,考虑到ρ函数的条件及把反对称估计作为抗差估计计算迭代的初始值 ,验证了定位参数的抗差估计值也一定是反对称估计 ,满足无偏性条件 ,得到定位参数的抗差估计是无偏估计。     

ON THE EVALUATION OF DEFLECTIONS OF THE VERTICAL USING FFT TECHNIQUE

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Efficient collection of training data for sub-pixel land cover classification using neural networks

Artificial neural networks (ANNs) are a popular class of techniques for performing soft classifications of satellite images. They have successfully been applied for estimating crop areas through sub-pixel classification of medium to low resolution images. Before a network can be used for classification and estimation, however, it has to be trained. The collection of the reference area fractions needed to train an ANN is often both time-consuming and expensive. This study focuses on strategies for decreasing the efforts needed to collect the necessary reference data, without compromising the accuracy of the resulting area estimates. Two aspects were studied: the spatial sampling scheme (i) and the possibility for reusing trained networks in multiple consecutive seasons (ii). Belgium was chosen as the study area because of the vast amount of reference data available. Time series of monthly NDVI composites for both SPOT-VGT and MODIS were used as the network inputs. The results showed that accurate regional crop area estimation (R² > 80%) is possible using only 1% of the entire area for network training, provided that the training samples used are representative for the land use variability present in the study area. Limiting the training samples to a specific subset of the population, either geographically or thematically, significantly decreased the accuracy of the estimates. The results also indicate that the use of ANNs trained with data from one season to estimate area fractions in another season is not to be recommended. The interannual variability observed in the endmembers’ spectral signatures underlines the importance of using up-to-date training samples. It can thus be concluded that the representativeness of the training samples, both regarding the spatial and the temporal aspects, is an important issue in crop area estimation using ANNs that should not easily be ignored.     

裂缝性储层的电各向异性响应特征研究

以水平裂缝分布的孔隙介质模型为基础,建立了水平裂缝面的平行裂缝分布和带粗糙表面的点接触裂缝模型,导出了两类裂缝模型的水平和垂直电阻率响应关系,分析了两类裂缝模型在没有围压和存在围压条件下的水平电阻率、垂直电阻率变化规律和电各向异性系数的变化特征.讨论了裂缝开度、裂缝密度和裂缝粗糙度等裂缝特征参数对裂缝性储层电各向异性的影响.为简化讨论,所有电性响应特征的分析都忽略了裂缝和孔隙表面的导电性和极化的影响.利用平行分布水平裂缝面模型和带粗糙面的裂缝模型,考察了围压条件下的裂缝性岩石的电各向异性响应特征,得到了对实际应用有意义的结果.     

K 近邻非参数回归概率预报技术及其应用

针对参数回归技术制作概率预报存在拟合好、但预报结果不稳定的现象, 提出了用K近邻非参数回归技术制作概率预报的新途径。K 近邻非参数回归技术包括历史样本数据库、近邻子集生成和优化以及预报量估计4 个主要部分。利用该技术进行了单要素概率预报(主要包括云量和降水)和多维联合概率预报(降水、总云量、风速和气温)试验, 并对试验结果进行了检验。实例研究结果表明:该文所给出的计算方案预报稳定性好, 准确率较高,具有良好的业务应用价值。     

比值—特征主成分混合分析提取金矿蚀变信息：以赣南遂川地区为例

在分析与金矿有关的两类蚀变岩的ＴＭ波谱特征的基础上,以赣南遂川地区为实验区,利用比值－特征主成分混合分析进行了金矿蚀变岩信息提取,为找矿预测和野外工作指明了方向。