顾及局部与结构特征的稀疏多项式逻辑回归高光谱图像分类方法

稀疏多项式逻辑回归在分类中仅利用图像光谱信息,导致分类效果不太理想.本文提出了一种顾及局部与结构特征的稀疏多项式逻辑回归高光谱图像分类方法.首先利用加权均值滤波与拓展形态学多属性剖面对原始高光谱图像进行局部与结构特征提取;然后对二者进行加权平均特征级融合以获取更具唯一性的像元特征;最后由稀疏多项式逻辑回归分类器对融合结果进行分类.结果表明,本文方法能有效地提高分类精度,而且具有较强的稳健性.     

融合GNSS和加速度计监测数据的超高建筑动态特性分析

GNSS和加速度计是目前动态监测超高建筑环境载荷变形的主要手段。GNSS具有无需通视、可直接获取三维位移等优点,但受精度和采样率的限制,其对微变形及高频振动信息不敏感;而加速度计具有高精度和高采样率等优点,但无法监测超高建筑低频的似静态变形。为充分发挥这两种传感器的各自优势,提出利用多速率Kalman滤波和RTS平滑方法对超高建筑GNSS和加速度计监测数据进行融合处理。试验结果表明,与单一的GNSS监测技术相比,该方法有利于削弱GNSS高频噪声的影响,提高位移数据的采样率,可有效识别超高建筑的低频和高频振动频率,提高对微变形振动的监测能力;与单一的加速度计监测技术相比,该方法可以准确监测超高建筑的低频变形信息,具有良好的工程应用价值。     

顾及有色噪声的自适应粒子滤波UWB定位算法

传统卡尔曼滤波算法要求噪声模型符合高斯分布,在UWB室内定位中,由于载体本身的机制等干扰,观测噪声不仅仅是白噪声,也存在有色噪声的情况,而粒子滤波可以处理有色噪声的问题。本文通过增加似然分布自适应调整来改进粒子滤波用于目标跟踪的精度,同时研究在白噪声、有色噪声下似然分布自适应调整粒子滤波和拓展卡尔曼滤波在UWB中的优势与不同。试验结果表明：观测噪声为白噪声时,拓展卡尔曼滤波和粒子滤波均可以较好地实现对行人的定位跟踪;观测噪声为有色噪声时,自适应粒子滤波定位效果优于粒子滤波、拓展卡尔曼滤波。     

一种顾及道路复杂度的增量路网构建方法

针对传统路网采集和更新需要昂贵的实地测量以及大量的后续内业处理问题,提出了一种从大规模粗糙轨迹数据中自动生成路网的方法。该方法包含轨迹滤选和路网增量构建两步:第1步通过构建空间、时间、逻辑约束的规则模型,在消除数据中的噪音和冗余的同时,将原始轨迹进行合理分割,滤选形成规范轨迹集合;第2步基于信息熵计算轨迹点周围道路的复杂度,据此自动调节道路分割参数,不断将新产生的路段加入到路网,同时计算道路平均交通流量和速度等路况信息,遍历各规范轨迹的定位点重复以上处理过程,最终得到完整路网。通过昆明市200辆出租车采集的约6851万条轨迹数据进行路网构建试验,并与OpenStreetMap数据比较,证明了本文方法的有效性。与已有典型方法比较,本文方法能用更少节点提取更高质量的路网。     

Robust adaptive unscented Kalman filter for bearings-only tracking in three dimensional case

This paper proposes an improved version of Unscented Kalman Filter (UKF), namely Robust Adaptive UKF (RAUKF), with a special focus on Bearings-Only Target Tracking for three-dimensional case (3DBOT). The automatic tuning of the noise covariance matrices and the robust estimation of the target states form a critical point for the performance of the Kalman-type filtering algorithms, especially in the variable environmental conditions exposed in underwater. The key idea of the proposed filter is to combine robust aspects of UKF and adaption of the process and measurement noise covariance matrices with low computational complexity. The main contribution of this paper is to adjust these matrices by means of the steepest descent algorithm, and the H_∞ technique is embedded to achieve superior performance in terms of accuracy and robustness against initial conditions and model uncertainties. Different experiments are performed to evaluate the performance of the proposed algorithm in the 3DBOT problem with a single moving observer. Simulations demonstrate that the proposed filter produce more accurate results with satisfactory computational burden in comparison with other methods.     

捷联式海洋重力数据的小波阈值滤波

基于捷联式海洋重力测量数据,对比不同小波、阈值及施加方式对滤波效果的影响。结果表明,db6、db7、db8、db9、db10、sym6、sym7、sym8、sym9、sym10、coif3、coif4和coif5小波较适用于海洋重力测量,小波分解层次可取至8层或9层,采用史坦无偏风险阈值滤波效果较好;小波分解至第8、9层时,阈值滤波结果与截止频率为0.005 Hz和0.003 3 Hz的Butterworth低通滤波器的滤波结果吻合较好,但小波阈值滤波结果更加平滑,两者差值的RMSE在0.25 mGal以内,且小波阈值滤波更容易分解出噪声成分,可以更有效地消除重力畸变。     

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.