全捷联图像导引头隔离度辨识及其抑制技术应用

针对全捷联图像导引头中传感器刻度尺误差与动力学偏差引起的隔离度问题,阐述了隔离度寄生回路产生的机理,并提出在线辨识探测器及角速率陀螺刻度尺系数与补偿导引头动力学延迟的隔离度抑制方案。建立导引头数字平台的等效模型,基于无迹卡尔曼滤波算法（UKF）,对传感器刻度尺系数以及真实弹目视线角速率进行辨识;运用匹配滤波器补偿导引头探测器动力学滞后。最后进行数学仿真,从稳定弹体飞行姿态与提升制导精度两个方面,对各方案的可行性进行了论证。结果表明:UKF滤波算法与匹配滤波器可以有效地对隔离度进行抑制并提升制导系统性能。     

Estimation of Regional Scale Evapotranspiration Through Analysis of Satellite Thermal-infrared Data

Since the mid 70's the LANDSAT series of satelites has acquired visible and near-infrared observations of the earth at a frequency and spatial resolution suitable for agriculture assessment purposes. More recently satellite systems have acquired high precision thermalinfrared data relating to surface thermal properties and moisture status. A data set from the Heat Capacity Mapping Mission [1] illustrates the potential applications of such data for inferring evapotranspiration on a regional scale. Methods described previously [2] are utilized to estimate evapotranspiration rates, yielding results which are consistent with surface measurements of pan evaporation.     

基于气象卫星数据的我国沿海悬空波导反演方法初步研究

悬空波导的测量方法有直接测量和间接测量两种.传统的直接测量手段只能得到单点的波导信息.间接测量也存在一定的局限性,不能获取高时空分辨率的波导多维空间信息.而静止气象卫星由于其高时空分辨率观测的特点,能对我国沿海海域大气环境进行时空四维监测.文中借鉴国外一种基于气象卫星数据反演悬空波导的方法,利用我国静止气象卫星FY-2G的云分类和云顶温度数据、地面观测数据及探空数据,分析该方法中各影响因素的敏感性,初步检验了该方法在我国沿海的适用性.提出建立本地海域悬空波导反演方法的几个关键问题：①选用高精度云顶温度反演数据;②选择合适规定层（850hPa或700hPa）;③根据本地数据得到一些参量的经验值;④建立本地参数化方案.下一步将寻找更多我国不同沿海海域的事例进行验证,该方法有希望运用到我国沿海大气波导监测中,为我国沿海大气波导监测和反演提供一种新方法新技术.     

A framework for analyzing and designing scale invariant remotesensing algorithms

The land surface exhibits heterogeneity across a range of spatial scales. Remote sensors provide integrated information at the pixel scale, however, there is important spatial variability at scales smaller than the scale of the sensor. On the other hand, large scale models that use remotely sensed data do not require them at the same spatial resolution at which remote sensors are required to operate. In this paper, a framework for testing aggregation-disaggregation properties of remote sensing algorithms is presented. The proposed framework provides a systematic approach for parameterizing the land surface heterogeneity effects. For the estimation of the pixel scale response, the lumped response should be modified by the variance and covariance terms. This representation of land surface heterogeneity could lead to substantial savings in remote sensing data storage and management. Using simulated land and vegetation scenarios, the authors have successfully parameterized subpixel scale heterogeneity effects for the estimation of vegetation index, by modeling the variances and covariance terms with the pixel scale values     

空间相关色噪声下基于酉变换的信号源数目估计

空间相关色噪声环境下的信号源数目的估计一直是个难题.本文在窄带信号条件下,利用信号的时间相关长度大于噪声的时间相关长度这一事实,提出了一种色噪声环境下基于辅助变量和酉变换的信号源数目估计方法.构造适当的辅助变量减轻甚至消除噪声对检测性能的影响.对信号子空间和噪声子空间的正交性进行了分析得出新的估计准则.最后通过计算机仿真和实测数据对比已有的其他方法验证了本文方法的有效性和优越性.     

On rate-constrained distributed estimation in unreliable sensor networks

We study the problem of estimating a physical process at a central processing unit (CPU) based on noisy measurements collected from a distributed, bandwidth-constrained, unreliable, network of sensors, modeled as an erasure network of unreliable "bit-pipes" between each sensor and the CPU. The CPU is guaranteed to receive data from a minimum fraction of the sensors and is tasked with optimally estimating the physical process under a specified distortion criterion. We study the noncollaborative (i.e., fully distributed) sensor network regime, and derive an information-theoretic achievable rate-distortion region for this network based on distributed source-coding insights. Specializing these results to the Gaussian setting and the mean-squared-error (MSE) distortion criterion reveals interesting robust-optimality properties of the solution. We also study the regime of clusters of collaborative sensors, where we address the important question: given a communication rate constraint between the sensor clusters and the CPU, should these clusters transmit their "raw data" or some low-dimensional "local estimates"? For a broad set of distortion criteria and sensor correlation statistics, we derive conditions under which rate-distortion-optimal compression of correlated cluster-observations separates into the tasks of dimension-reducing local estimation followed by optimal distributed compression of the local estimates.     

Estimation and monitoring of bare soil/vegetation ratio with SPOT VEGETATION and HRVIR

Covering soils with vegetation during the fallow and planting seasons is one of the main ways to reduce water pollution, by restricting pollutant fluxes to aquatic systems. The bare soil/vegetation ratio monitoring can be carried out daily with a coarse spatial resolution using SPOT VEGETATION (1 km). Nevertheless, land-cover changes detected at a regional scale with this ratio may be due to winter vegetation cover changes as well as the influence of climatic events. Therefore, observed changes have to be validated from a local-scale analysis with higher spatial resolution data. The aim of this study is to develop a technique that allows high or low variations detected at a regional scale to be assessed from SPOT VEGETATION images with data acquired at a higher scale, SPOT High Resolution Visible and Infrared images in our case. In this study, the link between the images from the two sensors is achieved from the design of an artificial neural network method based on a Kohonen self-organizing map. The originality of this method lies in the use of external knowledge from ground observations and the use of temporal behavior to solve such a change of scale. Results of testing this method by using a potential change map based on the last few years' land-cover observations have shown a good correspondence between the observed and predicted bare soil/vegetation balance with regards to the spatial resolution difference between the two sensors.     

Systems approach toward nationwide air-pollution control III. Mathematical models

The future air-pollution control system that is described in Parts I and II of this article will be equipped with sensors to monitor the current status of local air pollution and meteorological factors such as wind, stability, or mixing depth. The system will be furnished not only with data on the location and strength of pollution sources and synoptic weather conditions, but with any other information needed to forecast air quality for a period of 24 hours or more. Once these statistics have been compiled, however, it is the job of the mathematical model to convert the data into pragmatic decisions for controlling the air resource.     

Local frequency representations for robust multimodal image registration

Automatic registration of multimodal images involves algorithmically estimating the coordinate transformation required to align the data sets. Most existing methods in the literature are unable to cope with registration of image pairs with large nonoverlapping field of view (FOV). We propose a robust algorithm, based on matching dominant local frequency image representations, which can cope with image pairs with large nonoverlapping FOV. The local frequency representation naturally allows for processing the data at different scales/resolutions, a very desirable property from a computational efficiency view point. Our algorithm involves minimizing-over all rigid/affine transformations--the integral of the squared error (ISE or L2 E) between a Gaussian model of the residual and its true density function. The residual here refers to the difference between the local frequency representations of the transformed (by an unknown transformation) source and target data. We present implementation results for image data sets, which are misaligned magnetic resonance (MR) brain scans obtained using different image acquisition protocols as well as misaligned MR-computed tomography scans. We experimently show that our L2E-based scheme yields better accuracy over the normalized mutual information.     

A Practical Method for Retrieving Land Surface Temperature From AMSR-E Over the Amazon Forest

Remote sensing of land surface temperature (LST) using infrared (IR) sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), is only capable of retrieval under clear-sky conditions. Such LST observations over tropical forests are very limited due to clouds and rainfall, particularly during the wet season and high atmospheric water-vapor content. In comparison, low-frequency microwave radiances are minimally influenced by meteorological conditions. Exploring this advantage, we have developed an algorithm to retrieve LST over the Amazonian forest. The algorithm uses multifrequency polarized microwave brightness temperatures from the Advanced Microwave Scanning Radiometer on NASA's Earth Observing System (AMSR-E). Relationships between polarization ratio and surface emissivity are established for forested and nonforested areas, such that LST can solely be calculated from microwave radiance. Results are presented over three time scales: at each orbit, daily, and monthly. Results are evaluated by comparing with available air-temperature records on daily and monthly intervals. Our findings indicate that the AMSR-E-derived LST agrees well with in situ measurements. Results during the wet season over the tropical forest suggest that the AMSR-E LST is robust under all-weather conditions and shows higher correlation to meteorological data (r = 0.70) than the IR-based LST approaches (r = 0.42).     

Irrigation monitoring by following the water balance fromNOAA-AVHRR thermal infrared data

Thermal infrared data from NOAA-AVHRR are used to map surface temperature with pixels of 1 km². Physical relationships give evapotranspiration from surface temperature and readily available meteorological parameters. Maps of evapotranspiration are then made for areas where sugarcane is dominant and are used to follow the water balance and to manage the irrigation for this crop     

多传感器单模型动态系统多尺度数据融合

利用多尺度分析的思想,将基于模型的动态系统分析方法与基于统计特性的多尺度信号变换方法相结合,建立起目标状态基于多源观测信息的多尺度数据融合估计新算法,在每个尺度上获得目标状态基于全体细尺度上传感器测量信息的融合估计值,此算法可有效地应用于拥有不同采样速率的多传感器动态系统的数据融合估计.     

Parametric methods in the analysis of survival data

Parametric methods have been used extensively to analyze survival data. The parametric distribution that has been most widely used in fitting survival data is the exponential. However, the exponential survival distribution does not generally provide a satisfactory fit to survival data. One reason is that the hazard rate in survival studies is not usually constant with respect to time. In roughly the last decade, other survival distributions have been used to fit to survival data. They include the Weibull, gamma, lognormal and extreme value distributions. These distributions allow more flexibility than the exponential because their associated hazard rates are not constant with respect to time.Methods are presented for estimating the parameters of the survival distributions mentioned above in the cases for which the data are uncensored and censored. Furthermore, survival may be correlated with some other variable or variables such as age, weight, or a diagnostic characteristic such as white blood count. To account for the relationship between survival and the variables related to survival, survival models have been developed which take into consideration these concomitant variables. Methodology is also presented for estimating parameters of survival models that include concomitant variables.Finally, methods are discussed concerning growth in survival. For example, a clinical trial may be conducted in stages; each stage representing a treatment regimen that is an improvement over previous stage treatments. Reliability growth models are discussed in order to assess survival at all stages of the clinical trial that have been conducted and predict survival probabilities at future stages.     

Multithreshold Procedure for Evaluating Parameters of Meteorological Objects against the Background of Local Objects Clutter in Pulsed Doppler Weather Radars

A multithreshold procedure for estimating parameters of meteorological objects (MO) against the background of interferences from local objects in pulsed Doppler weather radars has been proposed. It is based on using the operation results of introduced detector of local objects for making a decision on estimating the parameters of meteorological objects before or after the nonadaptive MO suppression filter. The task of detection in this detector is solved by the threshold processing of correlation coefficients of reflections carrying the information on both the composition of analyzed mixture andMO parameters. This feature of the proposed detector compares favorably with a large variety of the known detectors. It is shown that the proposed detector makes it possible to take a correct decision about the composition of the mixture with probability D > 0.9 already at MO radial velocities V_r > 2 m/s. The possibility and expediency of practical use of the proposed procedure in the system of estimating the MO parameters under the real conditions of limited sample sizes of processed inputs were confirmed by mathematical experiment. In this case, the accuracy of estimates is shown to be more than twice as high as that of the known procedures.     

基于多尺度增强网络的人群计数方法

人群计数研究普遍使用欧几里得损失函数，易造成图像局部相关性缺失，且现有研究方法未能充分提取人群图像中连续变化的尺度特征，影响了人群计数模型的性能。针对上述问题，该文提出一种基于多尺度增强网络的人群计数模型(MSEN)。首先，在多分支结构生成网络中引入区域性判别网络，将二者组合形成嵌入式GAN模块，以增强生成图像的局部相关性；之后，基于金字塔池化结构设计了尺度增强模块，将该模块连接在嵌入式GAN模块之后，进一步从不同区域提取不同尺度的局部特征，以最大程度地应对人群图像局部尺度连续变化的问题，从而增强整体模型的泛化能力。最后，在3个具有挑战性的人群计数公共数据集上进行了广泛的实验。实验结果表明，该文所述模型可有效提升人群计数问题的准确性和鲁棒性。     

Retrieval, validation, and application of the 1-km aerosol optical depth from MODIS measurements over Hong Kong

The Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol retrieval algorithm was developed to derive aerosol properties at a global scale, suitable for climate studies. Under favorable conditions (clear sky and over dark surfaces), the standard 10/spl times/10 km MODIS aerosol products are also useful on regional scales to monitor aerosol distributions and transports. However, the 10-km resolution is insufficient to depict aerosol variation on local or urban scales, due to inherent aerosol variability as well as complex surface terrain. In this study, we have modified the MODIS algorithm to retrieve aerosol optical depth (AOD) at 1-km resolution over Hong Kong, a city of just over 1000 km/sup 2/ with very complex surface features. Accompanied by the increased spatial resolution are new aerosol models derived with single-scattering albedo (SSA) around 0.91-0.94 to accommodate higher aerosol absorption encountered in Hong Kong than that was presumed for MODIS standard products (SSA/spl sim/0.97) over the region. The derived AOD data are compared to handheld Microtops II sunphotometer observations at the Hong Kong University of Science and Technology and other locations across Hong Kong. Retrieval errors within 15% to 20% of sunphotometer measurements are found. Moreover, when compared with the standard 10-km AOD products, the 1-km AOD data are much better correlated with PM/sub 10/ measurements across Hong Kong, suggesting that the new 1-km AOD data can be used to better characterize the particulate matter distribution for cities like Hong Kong than the MODIS standard products.     

时间尺度的多分辨率综合

本文根据小波分析的基本原理,对原子钟信号进行多分辨分解,将分解后的小波变换系数进行加权平均,得到不同小波尺度综合原子时的加权平均小波变换系数,然后由小波变换的重构条件,反演综合时间尺度,由于对原子钟信号进行小波分解,利用不同尺度的小波变换系数的小波方差进行加权平均,这样既要考虑不同原子钟在稳定性方面的差异,又顾及同一台原子在钟在不同小波尺度的变化特性,最后根据陕西天文台国家授时中心的实测数据对这种     

Myocardial motion analysis from B-mode echocardiograms.

The quantitative assessment of cardiac motion is a fundamental concept to evaluate ventricular malfunction. We present a new optical-flow-based method for estimating heart motion from two-dimensional echocardiographic sequences. To account for typical heart motions, such as contraction/expansion and shear, we analyze the images locally by using a local-affine model for the velocity in space and a linear model in time. The regional motion parameters are estimated in the least-squares sense inside a sliding spatiotemporal B-spline window. Robustness and spatial adaptability is achieved by estimating the model parameters at multiple scales within a coarse-to-fine multiresoluion framework. We use a wavelet-like algorithm for computing B-spline-weighted inner products and moments at dyadic scales to increase computational efficiency. In order to characterize myocardial contractility and to simplify the detection of myocardial dysfunction, the radial component of the velocity with respect to a reference point is color coded and visualized inside a time-varying region of interest. The algorithm was first validated on synthetic data sets that simulate a beating heart with a speckle-like appearance of echocardiograms. The ability to estimate motion from real ultrasound sequences was demonstrated by a rotating phantom experiment. The method was also applied to a set of in vivo echocardiograms from an animal study. Motion estimation results were in good agreement with the expert echocardiographic reading.     

带反馈信息的多传感器分层估计算法

为了改善局部节点和传感器级的跟踪性能,本文研究带反馈信息的多级式多传感器系统中的状态估计技术.在给出有反馈信息情况下传感器级状态估计解的基础上,本文提出多坐标系中有反馈信息的两层集中、分布和混合估计方程.在不同笛卡尔坐标系中,本文提出了几种带反馈信息的三层多传感器系统中的航迹级融合方法,其中包括集-分估计、分-分估计和混-分估计,并以定理的形式证明有、无反馈信息情况下的两类三层状态估计是等价的、最优的.仿真结果表明,在多传感器信息融合系统中引入反馈机制可以明显改善一些局部节点和传感器级的跟踪精度.