Position and velocity reliability testing in degraded GPS signal environments

Reliability testing, namely receiver autonomous integrity monitoring (RAIM), consists of statistical testing of least-squares residuals of observations, e.g., on an epoch-by-epoch basis aiming towards reliable navigation fault detection and exclusion (FDE). In this paper, classic RAIM and FDE methods are extended with testing of range-rate residuals to find inconsistent velocity solutions in order to contribute to the reliability of the system with special focus on degraded signal environments. Reliability enhancement efforts discussed include a Backward-FDE scheme based on statistical outlier detection and an iteratively reweighted robust estimation technique, a modified Danish method. In addition, measurement weighting assigned to code and Doppler observations is assessed in the paper in order to allow fitting a priori variance models to the estimation processes. The schemes discussed are also suitable in terms of computational convenience for a combined GPS/Galileo system. The objective of this paper is to assess position and velocity reliability testing and enhancement in urban and indoor conditions and to analyze the navigation accuracy conditions with high sensitivity GPS (HSGPS) tests. The results show the necessity of weighted estimation and FDE for reliability enhancement in degraded signal-environment navigation.     

SAR图像多尺度积增强的目标检测算法

合成孔径雷达（SAR）成像系统的热噪声和海杂波严重影响SAR图像自动目标检测的性能,去噪和均匀背景杂波是提高SAR图像目标检测性能的重要课题。根据SAR图像噪声功率一般存在于信号小尺度,没有跨尺度特征,而目标信号的边缘具有跨尺度的特点,本文提出了一种多尺度积信号增强和去噪的SAR图像船舰目标检测算法。本算法对SAR图像进行小波变换,应用多尺度积在小波域增强SAR图像船舰信号和均匀背景杂波,再对SAR图像进行目标检测。ERS SAR图像用于验证本文算法。仿真实验结果表明,新算法同传统的双参数CFAR检测算法、基于K-分布背景杂波的检测算法以及基于小波软阈值增强的检测算法相比,在虚警数和品质因数性能指标上均优于后几种检测算法。     

一种高分辨率SAR图像快速目标检测算法

目标检测是自动目标识别(ATR)的第一个阶段。研究合成孔径雷达(SAR)图像目标检测问题,提出了一种基于Rayleigh分布的CFAR快速检测算法,将CFAR检测分成水平和垂直CFAR检测两步进行。利用相邻点参考窗口的重合及图像的分布特性,提高了参数估计的效率。算法同时利用目标方差特性以减少虚警率。对MSTAR数据进行实验,结果表明该算法具有较好的性能。     

2L-IHP目标检测算法及其在AIRSAR数据中的应用

简要介绍了2L—IHP（Two-Looks Internal Hermitian Product）算法，并针对实际情况对其作了相应的调整和改进。主要是突破算法对数据源的限制，使得它可以应用到幅度图像中。对于极化SAR图像，使用PWF（Polarimetric Whitening Filter）方云将3个通道的图像合成为一幅检测图，再进行目标检测。利用ADTS（Advanced Detection Technology Sensor）数据，对以上各种算法的检测效果进行了分析比较。应用C、L、P波段的AIRSAR极化数据进行试验，发现3个波段中，L和C波段的检测效果最好；4种极化方式中，HH极化取得最佳的检测效果；并且得出全极化数据的检测结果优于单极化数据的结论。但以上结论还有待进一步验证。     

Fast algorithm for maneuvering target detection in SAR imagery based on gridding and fusion of texture features

Designing detection algorithms with high efficiency for Synthetic Aperture Radar(SAR) imagery is essential for the operator SAR Automatic Target Recognition(ATR) system.This work abandons the detection strategy of visiting every pixel in SAR imagery as done in many traditional detection algorithms,and introduces the gridding and fusion idea of different texture fea-tures to realize fast target detection.It first grids the original SAR imagery,yielding a set of grids to be classified into clutter grids and target grids,and then calculates the texture features in each grid.By fusing the calculation results,the target grids containing potential maneuvering targets are determined.The dual threshold segmentation technique is imposed on target grids to obtain the regions of interest.The fused texture features,including local statistics features and Gray-Level Co-occurrence Matrix(GLCM),are investigated.The efficiency and superiority of our proposed algorithm were tested and verified by comparing with existing fast de-tection algorithms using real SAR data.The results obtained from the experiments indicate the promising practical application val-ue of our study.     

Fast algorithm for maneuvering target detection in SAR imagery based on gridding and fusion of texture features

Designing detection algorithms with high efficiency for Synthetic Aperture Radar (SAR) imagery is essential for the operator SAR Automatic Target Recognition (ATR) system. This work abandons the detection strategy of visiting every pixel in SAR imagery as done in many traditional detection algorithms, and introduces the gridding and fusion idea of different texture features to realize fast target detection. It first grids the original SAR imagery, yielding a set of grids to be classified into clutter grids and target grids, and then calculates the texture features in each grid. By fusing the calculation results, the target grids containing potential maneuvering targets are determined. The dual threshold segmentation technique is imposed on target grids to obtain the regions of interest. The fused texture features, including local statistics features and Gray-Level Co-occurrence Matrix (GLCM), are investigated. The efficiency and superiority of our proposed algorithm were tested and verified by comparing with existing fast detection algorithms using real SAR data. The results obtained from the experiments indicate the promising practical application value of our study.     

Using spectrum differentiation and combination for target detection of minerals

Among the techniques that have been developed in spectroscopy, derivative analysis is particularly promising for use with remote sensing data. In the first step of this research we apply the derivative spectrum in a real hyperspectral image and introduce a new target detection approach called “DCEM”. For this purpose, 1st to 5th orders of derivative spectrum were applied to the DCEM. The outcome of this research has shown that the application of derivative spectrum in target detection is perfectly advisable in a specific derivative order for each target. This order can be introduced as an optimized order or the Best DCEM. The spectrum differentiation eliminates low frequency components of the spectrum. Despite the little information included in those low frequency components of a signal or spectrum, their complete elimination cause an information loss problem. Hence, in the second step of this research an ensemble classifier approach was employed for the combined use of both spectra and the best derivative order. This simultaneous use of the derivative and zero order spectra is introduced as “ECEM”. Experiments were conducted via a HyMap hyperspectral airborne image in eastern Iran. The detection results show that both proposed methods significantly outperform CEM in ROC and AUC values. The best performance upgrade in DCEM detection was about 24% for Kaolinite target and about 28% for Alunite target in ECEM.     

GPS天-地无源双基地雷达探测海面移动目标

GNSS卫星作为一种照射源,可与地面接收机构成天-地无源双基地雷达系统,用于海面移动目标探测。但是,海面目标（如船只）反射的GNSS信号能量微弱,常被淹没在背景噪声和干扰信号中。针对这一问题,本文提出利用目标的运动特点聚集目标回波能量的目标探测方法。首先,将船只的运动轨迹看作合成孔径,采用SAR成像技术——距离多普勒算法（range-Doppler algorithm,RDA）实现目标回波能量的压缩聚集,同时抑制干扰信号（如海面杂波）。然后,使用相位梯度自聚焦算法（phase gradient autofocus,PGA）作自聚焦处理,进一步聚集目标回波能量。通过现场试验采集的数据验证了本文算法。试验结果表明,本文方法能够同时聚集多个目标回波能量,精确地估计目标到接收机的距离,并判断目标的移动方向。     

多特征融合的高分辨率遥感影像飞机目标变化检测

为利用高分辨率遥感影像实现高精度的飞机目标变化检测,提出了一种自适应的多特征融合变化检测与深度学习相结合的方法。首先,通过加权迭代的多元变化检测法获取变化强度图,并结合自适应的直方图统计法自动获取显著的变化与不变化样本;然后,提取多时相影像的光谱、边缘和纹理特征,完成多特征融合的变化检测,并通过形态学处理得到变化图斑;最后,利用训练的NIN(Network in Network)结构的卷积神经网络飞机识别模型,完成变化图斑的类型判别,实现变化飞机的检测。实验结果表明,本文方法在两组数据的正确率分别达到100%和91.89%,均优于对比方法,能实现准确可靠的飞机目标变化检测。     

小型集成地理信息系统建设中的若干问题

讨论了小型集成地理信息系统建设中的系统目标与作用。系统开发方案的选择，系统的多用户化与零时间维护方案等问题，同时还给出院 一些具体的解决方案。     

高光谱图像目标检测算法分析

本文将国内外的高光谱图像目标检测算法分为光谱异常检测、光谱匹配检测和高光谱与高空间分辨率结合目标检测三种检测算法,分析了三种检测算法的原理、应用特点和局限性,并探讨了目标检测算法的发展的可能性。     

一种复杂海天背景下的红外舰船目标自动检测方法

提出了一种红外舰船目标检测算法。首先,利用设计好的频域组合高通滤波器对原始红外图像进行处理,以得到舰船目标可能存在的区域即目标潜在区;然后,对获得的目标潜在区进行尺度自适应的局部阈值分割,进而提取出较为完整的舰船目标或者虚假目标;最后通过检测吃水线特征来筛选出正确的舰船目标。实验结果表明,此算法能够有效地检测出复杂海天背景条件下的红外舰船目标,且具有一定的时效性。     

空间构件形位检测方法与技术进展

精密工程与工业测量技术应用领域拓展及其与工业计量测试技术的交叉融合,衍生出"空间构件形位检测"技术、方法与系统。本文详细论述了"空间构件"与"形位检测"的定义及范畴,依测量原理将大尺寸空间构件形位检测技术归纳为正交系测量系统、空间支导线测量系统、三角法测量系统和球坐标法测量系统等,综述了各类测量系统和软件的技术进展,分析指出了狭长或超大空间、恶劣环境、目标动态变化等条件下的形位检测难题。     

利用双指数函数导数模型进行高分辨率卫星影像目标检测

提出了一种从高分辨率卫星影像中检测特定目标的方法。该方法首先用一种双边滤波的方法去除图像中的噪声,然后采用形态学处理的方法对图像进行分割和特征增强,并提取兴趣区域。在目标检测阶段,设计了一种基于双指数函数导数(DODE)模型的检测算子,该算子是面向具体目标设计的,能实现稳健的目标检测。对Google Earth中的影像分别进行了车辆检测实验和船舰检测实验,结果表明,此方法能够有效准确地检测高分辨率卫星影像中的特定目标。     

Generalized optimization of polarimetric contrast enhancement

A generalized optimization of polarimetric contrast enhancement (GOPCE) is proposed in this letter. For this problem, it is not only necessary to find the optimal polarization states such that the received power ratio of a desired target and clutter is maximal, but also necessary to find three optimal coefficients such that the ratio of two factors associated with the desired target and clutter is maximal, where both the factors consist of three parameters, i.e., the Cloude entropy and two special similarity parameters. The optimal coefficients of the GOPCE are obtained by solving an eigenvalue problem. Using an example, we demonstrate that the GOPCE can be employed for detecting roads in a forest area by using polarimetric synthetic aperture radar data.     

关于信息化测绘体系建设目标和任务的探讨

简要回顾了我国测绘体系的发展历程,并对传统测绘体系、数字化测绘体系和信息化测绘体系三个阶段的主要区别进行了概括,进而对信息化测绘体系的建设目标及主要任务进行了重点讨论。     

双通道SAR振动目标快速检测

目标的振动会对雷达回波产生特殊的相位调制,称为微多普勒效应,能够提供对微动目标检测的有利信息,因此对提高SAR(Synthetic Aperture Radar)系统性能具有重要意义。然而,已有的检测算法存在运算量大、抗杂波噪声能力弱和无法适应多振动目标等问题。针对这些问题,本文提出一种对振动目标检测的新算法。该算法利用相位中心天线偏置DPCA(Displaced Phase Center Antenna )对消技术消除杂波,并沿方位向累加DPCA信号来提高算法的抗噪声能力。由于振动目标SAR方位回波的频谱与脉冲序列具有高度相似性,本文算法选择了检测重复脉冲序列的脉冲重复频率PRI(Pulse Repetition Interval )变换法来实现振动目标的检测。仿真实验表明,本文算法能够在强杂波噪声条件下检测振动目标,同时具有准确振动频率估计性能,甚至当同一个单元存在多个振动目标时,本文算法依然适用。仿真中振动目标检测的计算机运行总时间不超过0.6 s,说明本文算法适用于实时检测,通过与GLRT算法和Hough变换算法运算量的比较,证明了本文算法相比于经典算法具有运算量小,检测速度快的优点。     

X波段散射计辐射计组合系统对树木背景中目标的观测研究

介绍Ｘ波段散射计辐射计组合系统的研制,利用这一组合系统可进行同时同地相继观测,得到ＶＶ,ＨＨ,ＶＨ,ＨＶ多极化的后向散射系σ＾０ｐｑ,Ｖ和Ｈ极化的辐射亮度温度ＴＢｑ和地面热发射率ｅｐ。给出了树木地面背景以及有多种金属目标的σ＾０ｐｑ－ＴＢｑ观测结果,并用一层面介质散射辐射的主被动遥感理论进行了分析,为地面主被动联合遥感和背景杂波中目标识别提供了一个实例研究。     

高光谱目标探测中的空间和光谱尺度效应

高光谱遥感目标探测主要利用目标和背景的光谱特征差异进行目标识别。一般情况下,影像的空间和光谱分辨率越高,探测效果越好。但多数情况下空间和光谱分辨率难以同时满足需求。针对该问题,本文利用Field Imaging Spectrometer System(FISS)地面高光谱成像仪器,通过在稀疏草地上布设人工绿色目标,研究了目标和背景光谱相似情况下,单一均匀背景下小目标探测问题,提出空间和光谱尺度定量分析方法,得到目标探测适用的空间和光谱尺度。结果表明:(1)利用FISS高光谱仪器进行人工目标探测,所需的空间分辨率约为目标尺寸的2倍以内;(2)当光谱分辨率优于40 nm时,目标和背景的两个主要特征:反射峰的位置和波段趋势差异均可被描述,在原始空间分辨率5倍(0.85 cm)以内,探测精度可以达到0.94以上。由于反射峰间距20 nm,当光谱分辨率低于40 nm时,该特征消失,造成探测精度的下降;(3)当光谱分辨率低于40 nm时,选取目标、背景光谱特征差异较大的波段可提高探测的有效性,在舍弃目标背景相似波段后,探测精度上升,得到本实验的最佳波段组合为红、绿、蓝、黄及红边波段。