基于时间序列叶面积指数稀疏表示的作物种植区域提取

以华北平原黄河以北地区为研究区域,以时间序列叶面积指数LAI(Leaf Area Index)傅里叶变换的谐波特征作为不同作物识别的数据源,利用稀疏表示的分类方法识别2007年—2016年冬小麦、春玉米、夏玉米等主要农作物种植区域。首先利用上包络线Savitzky-Golay滤波分别对2007年—2016年的时间序列MODIS LAI曲线进行重构,进而对重构的年时间序列LAI进行傅里叶变换,以0—5级谐波振幅、1—5级谐波相位作为作物识别的依据,基于各类地物的训练样本,通过在线字典学习算法构建稀疏表示方法的判别字典,对每个待测样本利用正交匹配追踪算法求解稀疏系数,从而计算对应于各类地物的重构误差,根据最小重构误差判定待测样本的作物类型,并对作物识别结果的位置精度进行验证。结果表明,2007年—2016年作物识别的总体精度为77.97%,Kappa系数为0.74,表明本文提出的方法可以用于研究区域主要作物种植区域的提取。     

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.     

基于灰关联分析方法卡尔曼滤波器的精度效能评估

针对综导系统卡尔曼滤波器的特点,深入研究了灰关联分析法在滤波器精度效能评估中的应用。给出卡尔曼滤波器进行灰关联分析的方法和步骤,并对某一类型的滤波器进行精度效能评估。仿真结果表明了该方法的可行性和有效性。     

Consideration of time-correlated errors in a Kalman filter applicable to GNSS

An algorithm for considering time-correlated errors in a Kalman filter is presented. The algorithm differs from previous implementations in that it does not suffer from numerical problems; does not contain inherent time latency or require reinterpretation of Kalman filter parameters, and gives full consideration to additive white noise that is often still present but ignored in previous implementations. Simulation results indicate that the application of the new algorithm yields more realistic and therefore useful state and covariance information than the standard implementation. Results from a field test of the algorithm applied to the problem of kinematic differential GPS demonstrate that the algorithm provides slightly pessimistic covariance estimates whereas the standard Kalman filter provides optimistic covariance estimates.     

观测值含有粗差的抗差粒子滤波算法

作为非线性滤波的代表,粒子滤波得到广泛应用。该算法通过随机生成的具有权重的样本（粒子）来计算后验概率密度。其中赋权的过程需要综合系统信息和观测信息,当观测值含有粗差时,会使粒子错误赋权影响滤波结果。提出一种新的基于抗差估计的抗差粒子滤波算法,通过计算粒子等价权,抑制观测值粗差的影响。模拟计算分析表明,当观测值含有粗差时,与标准粒子滤波相比,该方法能有效提高滤波精度。     

基于噪声随机模型的加权观测融合方法

信息融合技术中,在各局部传感器的有色观测噪声为一阶AR模型的情况下,可以利用观测扩增方法消除有色噪声的影响,得到最优加权观测融合方程,从而实现状态的最优滤波解。对于有色观测噪声为MA或ARMA模型的情况,观测扩增方法不再适用。提出了基于有色观测噪声随机模型级数展开的方法,求解出各局部传感器有色观测噪声的方差,并利用该方差对加权观测融合滤波器进行了构造。通过计算实例证明,该方法不仅适用于观测噪声为AR模型,同时适用于噪声MA或ARMA模型。     

基于惯导/天文高精度定姿方法

研究了利用惯性导航系统与天文导航系统进行组合定姿的方法;构造了惯导/天文组合导航系统的状态方程和量测方程;采用卡尔曼滤波技术设计了惯导/天文组合定姿算法。设计飞行器的典型飞行轨迹,通过数学仿真对该算法的有效性进行了验证。结果表明,天文导航能够有效地补偿惯性导航因陀螺漂移带来的误差,非常适用于飞行器的高精度定姿。     

改进Frost算子在SAR图像斑点噪声抑制中的应用

针对合成孔径雷达(SAR)图像斑点噪声滤波的重要性以及存在的问题,对Frost滤波算法参数进行分析,指出了该算法等权滤波器在处理含有细节结构特征时存在盲目平滑的缺点,进而提出了改进方法。利用灰度差值计算权值,综合考虑滤波窗口内的强度信息,在一定程度上解决了盲目平滑的问题。实验验证了改进算法的有效性,并以斑点噪声指数和边缘保持指数为评价准则,通过与Frost算法比较,对改进算法在去除噪声和边缘保持方面的性能做了客观评价。     

Two-dimensional,two-phase granular sediment transport model with applications to scouring downstream of an apron

We present a two-dimensional, two-phase model for non-cohesive sediment transport. This model solves concentration-weighted averaged equations of motion for both fluid and sediment phases. The model accounts for the interphase momentum transfer by considering drag forces. A collisional theory is used to compute the sediment stresses, while a two-equation (k–ε) fluid turbulence closure is implemented. A benchmark sediment transport problem concerning the scouring downstream of an apron is carried out as an example and numerical results agree with existing experimental data.     

基于Gabor变换和支持向量机的手写体字符识别算法

针对在手写字符识别中由于书写习惯和风格的不同造成字符模式不稳定的问题,将支持向量机SVM方法用于手写字符的识别.算法首先采用Gabor变换提取手写字符图像的特征参数,然后采用提取的特征训练SVM分类器.再应用SVM分类器分类和判别手写字符.实验表明这种方法具有良好的车牌识别效果,较强的鲁棒性,较大的应用价值.