GNSS接收机抗干扰自适应调零技术性能估计

GNSS抗干扰技术中常采用功率倒置算法(PI)来得到自适应波束形成的零陷。信干比为80dB时,PI算法能准确识别干扰的方向,抗干扰分辨率好,但当信干比降低到20dB左右时,在射频干扰信号方向谱周围会形成大量带状的零陷,干扰信号的分辨率恶化严重。空间谱估计中的多重信号分类(MUSIC)算法具备超分辨率特性,通过信号子空间和噪声子空间的正交功率最小化原理,采用空间二维谱峰搜索方位角和仰角,能够准确进行DOA估计,有效区分有用信号和干扰信号。在高信干比条件下,基于MUSIC算法的最小功率估计抑制深度明显好于传统的PI算法;在低信干比条件下,MUSIC-PI算法在干扰信号方向谱判别及零陷抑制方面依然有效,而传统的PI算法失效。计算机仿真结果验证了该方法在GNSS抗干扰领域的有效性和鲁棒性。     

Multi-Index Soil Moisture Estimation from Satellite Earth Observations: Comparative Evaluation of the Topographic Wetness Index (TWI), the Temperature Vegetation Dryness Index (TVDI) and the Improved TVDI (iTVDI)

Soil moisture estimation from satellite earth observation has emerged effectively advantageous due to the high temporal resolution, spatial resolution, coverage, and processing convenience it affords. In this paper, we present a study carried out to estimate soil moisture level at every location within Enugu State Nigeria from satellite earth observation. Comparative analysis of multiple indices for soil moisture estimation was carried out with a view to evaluating the robustness, correlation, appropriateness and accuracy of the indices in estimating the spatial distribution of soil moisture level in Enugu State. Results were correlated and validated with In-Situ soil moisture observations from multi-sample points. To achieve this, the Topographic Wetness Index (TWI), based on digital elevation data, the Temperature Vegetation Dryness Index (TVDI) and an improved TVDI (iTVDI) incorporating air temperature and a Digital Elevation Model (DEM) were calculated from ASTER global DEM and Landsat images. Possible dependencies of the indices on land cover type, topography, and precipitation were explored. In-Situ soil moisture data were used to validate the derived indices. The results showed that there was a positive significant relationship between iTVDI versus TVDI (R = 0.53, P value < 0.05), while in iTVDI versus TWI (R = 0.00, P value > 0.05) and TVDI versus TWI (R = ?0.01, P value > 0.05) no significant relationship existed. There was a strong relationship between iTVDI and topography, land cover type, and precipitation than other indices (TVDI, TWI). In situ measured soil moisture values showed negative significant relationship with TVDI (R = ?0.52, P value < 0.05) and iTVDI (R = ?0.63, P value < 0.05) but not with TWI (R = ?0.10, P value > 0.05). The iTVDI outperformed the other two index; having a stronger relationship with topography, precipitation, land cover classes and soil moisture. It concludes that although iTVDI outperformed other indices (TVDI, TWI) in soil moisture estimation, the decision of which index to apply is dependent on available data, the intent of usage and spatial scale.     

地形特征约束下的失真DEM修复方法

数字高程模型（DEM）在描述地形时会存在一定程度的失真现象,对失真DEM进行修复可以有效地提高其精度及保真度。本文针对DEM在描述规则地形时存在的失真现象,提出了用地形特征约束失真DEM形态结构的失真修复方法,以规则地形中的平地结构地形为例,进行了失真修复试验。首先根据地形特征确定平地地形的DEM应为平面结构,再采用最小二乘法（LS）及随机抽样一致性算法（RANSAC）对其平面方程进行参数估计,进而得到修复的DEM数据。试验结果表明,本文所提的修复方法有效可行,修复后的DEM数据不仅提高了精度,还能充分地反映原地形的地形特征。两种修复算法的修复能力相接近,当存在大量格网点时,都能达到很好的修复效果,但RANSAC更能适应高程异常的情况,鲁棒性更好。     

Earth observations during space shuttle mission STS‐34: 18–23 October 1989

Abstract

Although high‐resolution microwave synthetic aperture radar (SAR) sensors possess all‐weather capability for mapping soil moisture from spaceborne platforms, continuous temporal and spatial monitoring of this important hydrological parameter has been relatively limited. However, the recent launch of operational SAR sensors aboard various satellites have made possible synoptic soil moisture monitoring a reality. Such systems operate over a wide range of frequencies, look angles, and polarization combinations, and thus show synergistic advantages when combined for estimating soil moisture patterns. Two soil moisture inversion algorithms have been developed using as inputs radar backscattering data at L, S, and C bands in the microwave frequency range. These models have been tested using radar image simulation with speckle added. It is observed that the neural network algorithm yields superior results in mapping actual soil moisture patterns over the linear statistical inversion technique, although both models show comparable errors in soil moisture estimation. We infer that using statistical estimation errors alone for comparison purposes may lead to erroneous conclusions regarding the advantages of one soil moisture inversion algorithm over another.     

最优Tikhonov正则化矩阵及其在卫星导航定位模糊度解算中的应用

首先,用贝叶斯（Bayes）统计理论的观点,把未知参数看作随机变量,引入未知参数的无信息先验分布函数,从数学上推导了均方误差最小意义下的正则化矩阵;然后,结合最优正则化矩阵和快速截断奇异值算法,提出了一种新的正则化方法;最后,探讨了新方法在全球卫星导航系统（Global Navigation Satellite System,GNSS）模糊度解算中的应用。通过一组GNSS模糊度解算实验,比较了最小二乘（least squares,LS）方法、L曲线岭估计和新方法的性能。结果表明,新方法解算成功率略高于L曲线岭估计,远高于LS方法;计算耗时略大于LS方法,远小于L曲线岭估计。     

GF-4增强型地表反射率库支持法的气溶胶光学厚度反演

针对GF-4等国产卫星气溶胶光学厚度反演算法存在的地表反射率估计困难、云像元污染等问题,本文发展了一种增强型地表反射率库支持的气溶胶光学厚度反演方法,改进了云筛选与地表反射率确定方案,在考虑GF-4逐像元成像角度的情况下,使用6SV模型与MOD09-CMA数据对季度尺度上的GF-4 PMS传感器数据进行大气校正,提出了百分比最小值均值法建立地表反射率库,并据此建立了NDVI与红蓝反射率关系模型,根据地表反射率的分布特点,当NDVI小于0.2的时候使用地表反射率库估计地表反射率,而当NDVI大于0.2时,则使用NDVI来估计地表反射率。使用MOD04气溶胶模式时空分布确定气溶胶参数。在京津冀地区开展气溶胶光学厚度反演实验,使用Aeronet站点数据与MOD04产品对反演结果进行了对比验证,与Aeronet相关系数R为0.964,均方根误差RMSE为0.13,满足±(0.05+0.2τ)的点多于78.9%,相关系数与均方根误差优于MODIS暗目标法产品,满足期望误差线的数量优于MODIS暗目标与深蓝算法产品。     

若干平滑和插值方法对图像空间分辨力估算的影响

图像空间分辨力是图像质量评估中的一个重要指标,而对图像空间分辨力的估算通常采用调制传递函数（ＭＩＦ）解算。由于图像噪声的存在和采样数据的离散性,平滑和插值是必不可少的两个环节,因此,平滑和插值算法的好坏直接影响最后的评估结果。采用了两种最常用的平滑算法和两种插值算法,并按８种实验方案对两幅典型的图像进行了实验对比研究,获得了这两种算法对图像空间分辨力评估的影响机理,从而遴选出了较好的平滑和插值算法     

一种基于少通道准校正GNSS干扰信号的空间谱测向方法

针对导航干扰信号测向初始相位估计难度大导致空间谱测向精度低、误差大的问题,提出了一种以经典多重信号分类(MUSIC)算法为基础的“少通道准校正空间谱测向方案”. 该方案是在每个通道内加入校准源,从而获得任意两个通道的初始相位差,减小了通道间的初始相位差对协方差矩阵计算的影响,实现空间谱测向的目的. 将所提的方案应用于五阵元三通道测向天线并在试验场地进行测试,实验结果表明:该方法能够解决复杂场景下单信号、双信号的测向,在低信噪比(SNR)的情况下,测向误差仍能保持在3.5°左右,测向精度较高,测向结果与全通道的测向结果基本一致.      

基于DFT模型的大场景InSAR图像配准

图像配准是实现干涉合成孔径雷达(InSAR)高精度相位提取及地形高程反演的关键,大场景图像的高效高精度配准成为近年高分宽幅InSAR成像应用研究的难点问题之一。由于大场景图像中不同区域偏移量及变化规律差异较大,传统最大相干系数配准方法需多分块及插值处理,面临计算量大且配准精度低等问题。针对此问题,本文提出一种基于DFT模型的大场景InSAR高效高精度图像配准算法。该方法利用最小均方差准则构建InSAR复图像配准的DFT模型,采用四叉树自适应分块及矩阵相乘DFT快速重采样配准方法,实现大场景InSAR图像各子块区域的高效高精度亚像素配准。仿真和实测数据验证本文算法的有效性,结果表明该算法不仅可实现大场景InSAR复图像亚像素级配准,还具有较高的运算效率,其运算效率相对于传统FFT配准方法通常可提升3倍以上。     

PolInSAR森林高度反演中方法及波段选择引起的不确定性分析

森林高度是反映森林资源数量和质量的重要参数,极化干涉合成孔径雷达PolInSAR （Polarimetric Synthetic Aperture Radar Interferometry）技术在森林高度反演中极具潜力。由于森林散射特征受波长影响明显,由此引起的散射机理差异使得基于PolInSAR技术反演的森林高度结果具有很大的不确定性。为了定量化该不确定性的影响,本文以模拟森林场景为例,对PolInSAR技术森林高度反演中常用的4种方法——极化相位中心高度估测法、复相干相位中心差分法、复相干幅度反演法以及相干幅度、相位联合反演法,以及它们在常用的4个微波波段P、L、C和X中的森林高度估测结果进行了分析;明确了匀质森林场景中,算法、波段选择引起的森林高度估测结果的不确定性。研究结果表明：在森林场景基本一致的情况下,估测算法的选择直接影响森林高度估测结果,其中复相干幅度反演法在4个波段的估测结果中精度均最高,但各估测点的估测结果离散度及不确定度较大。波长对4类估测方法估测结果的影响差异明显：复相干幅度反演法的反演结果几乎不受波长的影响,而相干幅度、相位联合反演法受波长影响明显,在P和L波段反演结果中精度较高,在C和X波段反演结果中精度降低明显。此外,以传统的交叉极化（HV）相位代表冠层散射相位中心,水平同极化与垂直同极化的相位差（HH-VV）代表地表散射相位中心,采用复相干相位中心差分法进行森林高度估测会出现严重低估现象。估测结果不确定度具有波长和算法选择依赖性,在C和X波段采用复相干相位中心差分法估测结果不确定度最低,在P和L波段采用极化相位中心高度估测法估测结果不确定度最低,而复相干幅度反演法估测结果则在多个波段中的不确定度均最高。     

Glacier surface velocity estimation using repeat TerraSAR-X images: Wavelet- vs. correlation-based image matching

For the observation and monitoring of glacier surface velocity (GSV), remote sensing is an increasingly suitable tool thanks to the high temporal and spatial resolution of the data. Radar sensors have the specific advantage over optical sensors of being nearly weather and time-independent.Two image pairs separated by 11 days, acquired with the high-resolution spotlight (HS) and stripmap (SM) modes of the German sensor TerraSAR-X, were used to estimate GSV over Switzerland’s Aletsch Glacier. The SM mode covers larger ground swaths, making it more suitable for glacier-wide observations, while the HS images cover less area but offer the highest-possible spatial resolution, approximately 1 × 1 m on the ground. The images were acquired during the summer to maximise feature visibility by minimal snow cover.GSV estimation was performed using two methods, the comparison of which was a major goal of this study: traditional cross-correlation optimisation and a dense image matching algorithm based on complex wavelet decomposition. Each method was found to have unique advantages and disadvantages, but it was concluded that for GSV monitoring, cross-correlation is probably preferable to the wavelet-based approach. While it generates fewer estimates per unit area, this is not necessarily a critical requirement for all glaciological applications, and the method requires less initial “tuning” (calibration) than the wavelet algorithm, making it a slightly better tool in operational contexts. Also, the use of the highest-resolution spotlight datasets is recommended over stripmap mode images when large-area coverage is less critical. The comparative lack of visible features at the resolution of the stripmap images made reliable GSV estimation difficult, with the exception of several small areas dominated by large crevasses.     

Prediction of UT1–UTC,LOD and AAM χ<Subscript>3</Subscript> by combination of least-squares and multivariate stochastic methods

This article presents the application of a multivariate prediction technique for predicting universal time (UT1–UTC), length of day (LOD) and the axial component of atmospheric angular momentum (AAM χ ₃). The multivariate predictions of LOD and UT1–UTC are generated by means of the combination of (1) least-squares (LS) extrapolation of models for annual, semiannual, 18.6-year, 9.3-year oscillations and for the linear trend, and (2) multivariate autoregressive (MAR) stochastic prediction of LS residuals (LS + MAR). The MAR technique enables the use of the AAM χ ₃ time-series as the explanatory variable for the computation of LOD or UT1–UTC predictions. In order to evaluate the performance of this approach, two other prediction schemes are also applied: (1) LS extrapolation, (2) combination of LS extrapolation and univariate autoregressive (AR) prediction of LS residuals (LS + AR). The multivariate predictions of AAM χ ₃ data, however, are computed as a combination of the extrapolation of the LS model for annual and semiannual oscillations and the LS + MAR. The AAM χ ₃ predictions are also compared with LS extrapolation and LS + AR prediction. It is shown that the predictions of LOD and UT1–UTC based on LS + MAR taking into account the axial component of AAM are more accurate than the predictions of LOD and UT1–UTC based on LS extrapolation or on LS + AR. In particular, the UT1–UTC predictions based on LS + MAR during El Niño/La Niña events exhibit considerably smaller prediction errors than those calculated by means of LS or LS + AR. The AAM χ ₃ time-series is predicted using LS + MAR with higher accuracy than applying LS extrapolation itself in the case of medium-term predictions (up to 100 days in the future). However, the predictions of AAM χ ₃ reveal the best accuracy for LS + AR.     

利用双天线商用WiFi信道状态信息估计到达角

商用WiFi接收模块可以提供比接收无线信号强度指示（received signal strength indication,RSSI）更细粒的信道状态信息（channel state information,CSI）,利用3根天线获取CSI进行方位到达角（angle of arrival,AOA）估计已成为现实。利用正交频分复用技术（orthogonal frequency division multiplexing,OFDM）将2根天线拓展为60个虚拟天线阵,将前向平滑算法拓展到二维前向平滑算法。利用仿真的非相干信号源和相干信号源数据进行实验,结果表明,在只利用2根接收天线的前提下也能实现基于商用WiFi信号的方位角的AOA估计,所提出的2根天线的虚拟天线阵模型和二维前向平滑算法具有有效性和适用性。     

内陆水体藻蓝蛋白遥感反演研究进展

藻蓝蛋白(PC),作为蓝藻的标志性色素,通常被用作进行蓝藻水华遥感监测的标志物.近年来,内陆水体水质恶化,富营养化加剧,藻华频发,PC遥感反演研究越来越受到关注.本文从PC光学特性、反演算法开发、卫星传感器应用等方面难点及干扰因素,综合梳理了过去30 a PC遥感反演研究的发展历程和趋势,以期理解国内外相关研究的新思路...     

后向投影算法的地理坐标系下的合成孔径雷达成像

传统的机载合成孔径雷达(SAR)一般依赖于GPS/INS组合系统补偿平台的运动误差,并获得高分辨率的图像。GPS获取的地理坐标(即经度,纬度和高度)需要转换为本地的直角坐标,原始雷达数据在这个直角坐标系下才能引入成像及运动补偿算法进行处理,并获得本地直角坐标系下的图像。这种图像是通过局部坐标描述的,对于其他部门是不通用的,因为其他部门需要的是经过全球地理坐标标绘的图像。本文提出了一种由后向投影算法BP(Back Projection)引出的新的SAR成像算法,它直接在地理坐标下处理,可避免坐标转换的过程。而且生成德图像像素均和地理坐标一一对应,能够很方便地被情报或其他部门使用。而且在仿真试验以及外场试验中,证明了本文算法和常规BP算法成像效果是相当的。     

Improved Image Pansharpening Technique using Nonsubsampled Contourlet Transform with Sparse Representation

Multispectral (MS) and panchromatic (PAN) images contains complementary information. High spatial and spectral resolution is a prerequisite for images to be useful, which can be achieved through image pansharpening. In this paper, we propose a new pansharpening technique which is a combination of nonsubsampled contourlet transform (NSCT) and sparse representation (SR), called NSCT–SR. NSCT is a shift-invariant version of the contourlet transform which combines nonsubsampled pyramid (NSP) and the directional filter banks. NSP splits input MS and PAN images into low-pass and high-pass sub-bands. Fusion of high-pass sub-bands is done using local energy information while low-pass sub-bands are fused using SR. Finally, fused low-pass and high-pass sub-bands are combined to obtain image with high spatial and high spectral resolution. We have quantitatively compared NSCT–SR with other multiresolution algorithms by calculating spatial and spectral quality parameters. It is observed that spatial quality is improved by 0.93 % (for seaside image) and 1.54 % (for urban image). While spectral quality is improved maximum up to 31.39 and 40.47 %, for respective images. NSCT–SR also compared with other state-of-art algorithms by calculating various performance parameters including quality with no reference. It is found that, overall; NSCT–SR performs better compared to algorithms considered in work.     

Recovery of elevation from estimated gradient fields constrained by digital elevation maps of lower lateral resolution

Depth measurement techniques like stereo analysis and laser range scanning often yield a lateral resolution below image resolution. In contrast, shading-based methods estimate the elevation model at image resolution. We present a computationally efficient approach to recover a surface of high vertical and high lateral resolution from a noisy gradient field and independently measured elevation data of lower lateral resolution, relying on a minimization of the mean squared difference between the low-pass component of the surface obtained based on shading information and that of the independently measured elevation data. The presented method is compared to a reference approach that minimizes a weighted sum comprised of the mean squared difference between the low-pass components of the estimated gradient field and the optimized model, respectively. The presented algorithm is applied using lunar orbital image data and stereo elevation data and is evaluated regarding orbital laser altimeter measurements of high vertical accuracy.     

AMSR-E/2观测辐射值反演水成物参数

基于先进的微波扫描辐射计AMSR-E/2观测的辐射值,利用-维变分算法(1D-Var)反演各类水成物(云水、雨水和云冰)的垂直廓线,并对其反演结果进行检验。以2014年8月台风"夏浪"为例,分两步对变分反演的云微物理参数进行了检验。首先,将反演的各类水成物含量补充到辐射传输模式的输入场,观测算子模拟的AMSR-2各通道亮温与实况观测相比非常接近,可以很好地模拟出台风外形、强度及螺旋结构。其次,将反演的水成物廓线与载在Cloud Sat上的云雷达CPR同时段观测的雷达反射率因子进行对比,发现反演出的云水、雨水含量大值区与毫米波云雷达观测的雷达反射率因子高值区一一对应,进一步说明1D-Var反演的水成物参数精度很高。然而,由于星载AMSR-E/2观测通道少且空间分辨率低,对尺度较小、较薄的云不敏感,同时对云层较厚的密闭云区和多层云区反演能力也有限。     

Topographic correction of ALOS-PALSAR images using InSAR-derived DEM

Topographic corrections of synthetic aperture radar (SAR) images over hilly regions are vital for retrieval of correct backscatter values associated with natural targets. The coarse resolution external digital elevation models (DEM) available for topographic corrections of high resolution SAR images often result into degradation of spatial resolution or improper estimation of backscatter values in SAR images. Also, many a times the external DEMs do not spatially co-register well with the SAR data. The present study showcases the methodology and results of topographic correction of ALOS-PALSAR image using high resolution DEM generated from the same data. High resolution DEMs of Jaipur region, India were generated using multiple pair SAR images acquired from ALOS-PALSAR using interferometric (InSAR) techniques. The DEMs were validated using differential global positioning system measured elevation values as ground control points and were compared with photogrammetric DEM (advanced spaceborne thermal emission and reflection radiometer – ASTER) and SRTM (Shuttle Radar Topography Mission) DEM. It was observed that ALOS-PALSAR images with optimum baseline parameters produced high resolution DEM with better height accuracy. Finally, the validated DEM was used for topographic correction of ALOS-PALSAR images of the same region and were found to produce better result as compared with ASTER and SRTM-DEM.     

森林地上生物量遥感反演方法综述

森林地上生物量反演对理解和监测生态系统及评估人类生产生活的影响有着重要作用,日益发展的遥感技术使全球及大区域的生物量估算成为可能。近年来,不同的遥感技术和反演方法被广泛用于估算森林生物量。本文首先总结了现有的全球及区域生物量产品及其不确定性,然后综述了3类方法在森林地上生物量遥感反演中的应用,即基于单源数据的参数化方法、基于多源数据的非参数化方法和基于机理模型的反演方法,阐述了各类反演方法的特点、优势及局限性。最后从机理模型研究、多源遥感数据协同、生物量季节变化研究和遥感数据源不断丰富4个方面对今后的生物量遥感反演研究进行了展望。