Kalman-CS非均匀性场景校正算法的研究

多元探测器的应用必然会给遥感图像带来非均匀性,这是目前国内外遥感界竞相研究的领域。本文将Kalman滤波器校正算法与常数统计(CS)校正算法相结合,提出了Kalman-CS校正算法。对美国TERRA卫星MODIS图像进行的校正试验表明:采用Kalman-CS校正算法能有效去除遥感图像中的非均匀性条带,校正后的图像品质显著提高。     

卡尔曼滤波光谱法用于化学平衡研究

提出了光谱法与卡尔曼滤波及因子分析相结合的新方法以研究化学平衡,将复杂的化学平衡视为特殊的多组分混合体系,其中各物种在不同介质中存在相互的平衡,卡尔曼滤波及因子分析被用作有效的化学计量学分辨工具,从光谱数据中提取信息,已用于确定所存在的物种数目和浓度以及平衡常数,本文以部分三苯甲烷试剂为例确定其酸碱平衡常数。     

国产滤纸室温燐光背景的考察和降低背景方法的研究

固体基质室温燐光法(SS-RTP)的检出限依赖于固体基质的RTP背景的高低。本文考察了七种国产滤纸在重原子存在时和不存在时的RTP背景。研究了各种降低滤纸背景的处理方法。研究结果表明,采用NaOH、β-环糊精、EDTA、乙醇和丙酮浸泡或煮沸滤纸,都能不同程度的降低滤纸背景,最大降低率为88％。KI、NaAc、Pb(Ac)_2、TlNO_3和Cscl等重原子可使滤纸背景有较大幅度的增强,而经处理的滤纸,RTP背景较未处理滤纸背景降低一倍以上,最大可降低94.5％。用NaOH溶液处理滤纸,不仅能有效地降低背景,而且能提高分析物的RTP信号。研究表明纤维素伴生物质——木素,可能是产生燐光背景的杂质之一。     

Characterization of a Stable Adaptive Calibration Model Using Near-Infrared Spectroscopy and Partial Least Squares with a Kalman Filter

The calibration model of near-infrared (NIR) spectra established using the Kalman filter-partial least square (partial least squares combined with a Kalman filter) method can be adapted to outdated equipment, environmental changes, external samples, and other applications. However, the variance of the measurement noise estimation for NIR spectrum measurements cannot be easily obtained using Kalman filter-partial least squares; therefore, the variance in the measurement noise is often assumed to be zero for the Kalman filter-partial least square calibration model, which affects the stability of the model. In this study, the measured input and output data were used effectively, and the gamma test method for estimating the measurement noise variance was used to improve the stability of the Kalman filter-partial least square calibration model. First, an accurate estimation of the measurement noise variance was obtained, and accurate modeling was then performed using Kalman filter-partial least squares. Finally, 600 abandoned drilling fluid samples were used to confirm the validity of the proposed method. The Kalman filter-partial least square and gamma test-Kalman filter-partial least square methods are compared. Testing of external samples 401–600 demonstrated that the stability of the Kalman filter-partial least square model decreased. The root mean square error of the prediction of the Kalman filter-partial least square model was 27.135, which was worse than that of the gamma test-Kalman filter-partial least square model (20.307). The validation results show that the proposed method has better stability in tracking the evolution of the NIR spectrometer’s measurement state.     

Multi-Model Communication and Data Assimilation for Mitigating Model Error and Improving Forecasts

Models for weather and climate prediction are complex, and each model typi-cally has at least a small number of phenomena that are poorly represented, such as perhaps the Madden-Julian Oscillation (MJO for short) or El Ni\~{n}o-Southern Oscillation (ENSO for short) or sea ice. Furthermore, it is often a very challenging task to modify and improve a complex model without creating new deficiencies. On the other hand, it is sometimes possible to design a low-dimensional model for a particular phenomenon, such as the MJO or ENSO, with significant skill, although the model may not represent the dynamics of the full weather-climate system. Here a strategy is proposed to mitigate these model errors by taking advantage of each model''s strengths. The strategy involves inter-model data assimilation, during a forecast simulation, whereby models can exchange information in order to obtain more faithful representations of the full weather-climate system. As an initial investigation, the method is examined here using a simplified scenario of linear models, involving a system of stochastic partial differential equations (SPDEs for short) as an imperfect tropical climate model and stochastic differential equations (SDEs for short) as a low-dimensional model for the MJO. It is shown that the MJO prediction skill of the imperfect climate model can be enhanced to equal the predictive skill of the low-dimensional model. Such an approach could provide a route to improving global model forecasts in a minimally invasive way, with modifications to the prediction system but without modifying the complex global physical model itself.     

光纤陀螺随机误差的滤波方案的探讨

范跃祖，宁文如，刘剑．光纤陀螺随机误差的滤波方案的探讨．数理统计与管理，１９９７，１６（４），２９～３２．光纤陀螺是新一代的光学陀螺仪，它的性能决定着惯性参照系统的精度。光纤陀螺的误差分为两大类。一类是有规律的，另一类是随机的。本文提出了两种新的用于光纤陀螺随机误差补偿的Ｋａｌｍａｎ滤波方案，考虑了两种随机模型：（Ⅰ）角速度变化为等随机的；（Ⅱ）角速度变化为等随机加速的。仿真结果表明，第二种方案对噪声起到了很好的抑制作用和滤波效果     

Extended Kalman Filter-based localization algorithm by edge computing in Wireless Sensor Networks

The Extended Kalman Filter (EKF) has received abundant attention with the growing demands for robotic localization. The EKF algorithm is more realistic in non-linear systems, which has an autonomous white noise in both the system and the estimation model. Also, in the field of engineering, most systems are non-linear. Therefore, the EKF attracts more attention than the Kalman Filter (KF). In this paper, we propose an EKF-based localization algorithm by edge computing, and a mobile robot is used to update its location concerning the landmark. This localization algorithm aims to achieve a high level of accuracy and wider coverage. The proposed algorithm is helpful for the research related to the use of EKF localization algorithms. Simulation results demonstrate that, under the situations presented in the paper, the proposed localization algorithm is more accurate compared with the current state-of-the-art localization algorithms.     

改进的交互式多模型粒子滤波跟踪算法

通常的交互多模型卡尔曼滤波(IMMKF)或交互多模型扩展卡尔曼滤波(IMMEKF)对于非高斯问题无能为力;对于非线性问题,其性能不及交互多模型粒子滤波算法(IMMPF)。粒子滤波能够处理非线性/非高斯问题,其与交互式多模型结合用来获得更好的跟踪性能。然而,粒子滤波的主要问题是巨大的计算量,由于粒子滤波通常采用大量的粒子数目,将带来很大的计算负荷。该文提出了一种改进的交互多模型粒子滤波算法,其利用多模型综合使用了卡尔曼滤波和粒子滤波,与常规交互式多模型粒子滤波(IMMPF)相比,大大改善了计算效率。对于非线性/非高斯问题,其性能与IMMPF相当;对于线性问题,其性能与IMMEKF相当,并优于IMMPF的性能。     

Algorithm of SAR satellite attitude measurement using GPS aided by kinematic vector

In this paper,in order to improve the accuracy of the Synthetic Aperture Radar (SAR) satellite attitude using Global Positioning System (GPS) wide-band carrier phase,the SAR satellite attitude kinematic vector and Kalman filter are introduced.Introducing the state variable function of GPS attitude determination algorithm in SAR satellite by means of kinematic vector and describing the observation function by the GPS wide-band carrier phase,the paper uses the Kalman filter algorithm to obtian the attitude variables of SAR satellite.Compared the simulation results of Kalman filter algorithm with the least square algorithm and explicit solution,it is indicated that the Kalman filter algorithm is the best.     

基于雷达的嵌入式车载辅助驾驶系统的设计

提高车辆驾驶的安全性和舒适性是智能交通系统(ITS)的主要研究内容之一.介绍了自行研制的车载辅助驾驶系统,论述了其基本设计思想和总体方案.对系统的硬件实现、基于模糊推理的辅助驾驶系统的控制算法和雷达信号的Kalman滤波等技术进行了研究.经过现场试验证明该系统是切实可行的,在汽车行进过程中具有安全距离保持的功能,并且较好地体现了驾驶员的驾驶特征.