ʱ���������д�����ֱ�ֲ��ӳ�У���о�

????????????????InSAR?????????????????????????α?????????????????????????????????????????????????????????????????????α???????????Щ???????????????????????α?????????????α???λ??????????????????Las Vegas??????о????????????????????????????????????????????????????????????????????????Ч??????????????е??????????????     

卡尔曼滤波在动态测量系统中的应用

文中详细论述卡尔曼滤波的数学模型及其初值确定方法,引入动态测量系统应用实例,通过对数据噪音特性的分析,准确确定了卡尔曼滤波模型,利用matlab编程实现了相应功能。实例分析结果表明,卡尔曼滤波预测精度高,适用于大坝的实时监测与预报。     

一种改进的GNSS／INS非相干深组合导航

针对GNSS／INS非相干深组合导航中通道滤波器状态模型中信号幅值估测不准而导致跟踪环路观测噪声较大的问题,提出了一种改进的GNSS／INS非相干深组合导航方法。该方法通过去除通道滤波器中信号幅值状态变量,增加载波相位变化率加速度误差,重新设计了通道滤波器数学模型。仿真结果表明：相比于传统的非相干深组合导航方法,这种滤波器的跟踪性能与导航精度有了进一步提高。     

An effective thin cloud removal procedure for visible remote sensing images

Clouds are obstructions for land-surface observation, which result in the regional information being blurred or even lost. Thin clouds are transparent, and images of regions covered by thin clouds contain information about both the atmosphere and the ground. Therefore, thin cloud removal is a challenging task as the ground information is easily affected when the thin cloud removal is performed. An efficient and effective thin cloud removal method is proposed for visible remote sensing images in this paper, with the aim being to remove the thin clouds and also restore the ground information. Since thin cloud is considered as low-frequency information, the proposed method is based on the classic homomorphic filter and is executed in the frequency domain. The optimal cut-off frequency for each channel is determined semi-automatically. In order to preserve the clear pixels and ensure the high fidelity of the result, cloudy pixels are detected and handled separately. As a particular kind of low-frequency information, cloud-free water surfaces are specially treated and corrected. Since only cloudy pixels are involved in the calculation, the method is highly efficient and is suited for large remote sensing scenes. Scenes including different land-cover types were selected to validate the proposed method, and a comparison analysis with other methods was also performed. The experimental results confirm that the proposed method is effective in correcting thin cloud contaminated images while preserving the true spectral information.     

改进的UKF在GPS/INS组合导航中的应用

在GPS/INS组合导航中,传统UKF(Unscented Kalman Filter)计算量大,无法满足实时性要求。而且当动力学模型受到异常扰动误差影响时,其精度与稳定性易受到影响。针对以上问题,利用最小偏度单形采样策略降低UKF计算量以提高精度;通过自适应调整过程噪声以降低动态异常扰动误差对UKF精度与稳定性的影响。由此提出了一种改进UKF算法,用于GPS/INS组合导航。仿真实验结果表明,改进UKF算法用于GPS/INS组合导航的精度要优于UKF算法。     

Assessment of number and distribution of persistent scatterers prior to radar acquisition using open access land cover and topographical data

Persistent scatterer synthetic aperture radar interferometry (PSI) is a powerful remote sensing technique to detect and measure deformation of the Earth‘s crust – such as subsidence and landslides – with an accuracy of a few millimeters. Deformation is measured at specific points in a radar image called persistent scatterers (PS), which are characterized by long-term constant backscattering properties (high coherence) of the radar signal. Reliable PSI processing requires a stack of 15–50 SAR images and more, and processing is time-consuming (computational costs) and expensive (referring to both, costs for the SAR data and labor costs). Previous research for PS assessment used already acquired SAR data. This paper presents two new methods for predicting PS prior to the radar recording of the area of interest using freely available or low-cost land cover data, topographical maps and OpenStreetMap data. In the procedure, the distance between the assessed PS is calculated and classified regarding to the applicability for PSI processing. Additionally, the dispersion of the assessed PS within the site is analyzed. The results of the two assessment methods are validated using data of real PSI processing. Here, we show that the developed PS assessment techniques are fast and reliable tools to test the spatial applicability of PSI.     

利用Vondrak平滑处理PPP时间传递的随机噪声

利用精密单点定位（Precise Point Positioning,PPP）获得的时间传递结果受到非模型化误差和观测噪声的影响,这些误差和噪声表现出随机噪声特性,因此,对时间传递数据进行平滑是一项非常重要的任务.将天文数据处理中广泛应用的Vondrak平滑方法应用于PPP时间传递的消噪中,采用观测误差法选取较为合理的平滑因子,并对实测数据进行平滑处理,结果表明：Vondrak平滑法可有效地滤除PPP数据的随机噪声,不仅可以提高时间传递的精确度,也能明显改善PPP时间传递所体现的频率稳定度.     

航空摄影测量中POS系统高精度定位技术

航空摄影测量中摄影成像受飞行载体运动影响较大,因而对摄影机载运动测量传感器POS系统提出较高的精度要求。文中针对POS系统在航空摄影测量上的应用需求和特点,对POS系统的误差模型进行推导和分析,提出一种适用于POS的"双向滤波"算法。算法由前向卡尔曼滤波和反向固定区间平滑滤波算法(Rauch-Tung-Striebel,R-T-S平滑)组成,由此建立POS系统数据融合优化滤波器。通过实际飞行数据对算法进行验证,结果表明双向滤波算法定位精度优于传统卡尔曼滤波。     

金坪子滑坡动态稳定性分析研究

外界因素是影响滑坡变形和稳定状态的重要因素。进行滑坡的稳定性分析,依据监测资料进行预警预报,都有必要考虑外界因素的作用。以云南省昆明市的金坪子滑坡为背景,运用GIS及3DSlopeGIS的三维极限平衡分析方法,从地震、库水位、降雨三个外界因素单独或复合作用下的安全系数的变化情况,研究滑坡的动态稳定性。研究表明,在外界因素的作用下,通过分析安全系数—地震烈度曲线、安全系数—库水位高度曲线以及安全系数—时间曲线,可以定量判断地震、降雨、库水位对滑坡的影响,以及三者对滑坡影响力的高低顺序,为工程的预警预报提供重要参考。     

一种改进的去噪阈值混合滤波算法

中值滤波和均值滤波通常被分别用来处理脉冲噪声和高斯噪声,但当图像同时存在高斯噪声和脉冲噪声时,单独用任何一种滤波方法都不能达到最好的去噪效果。针对这一问题,本文提出了一种改进的基于去噪阈值的图像混合滤波算法,可以更有效地减少噪声,又可以较好地保持图像的边缘细节信息。