守恒律方程的导数人工压缩法

将高分辨率差分格式用于守恒律方程的导数方程,可以克服传统高分辨率格式在极值点精度退化的缺点,类似于Harten的人工压缩法,新方法称为导数人工压缩法.本方法既能提高间断的分辨率,又能提高极值点处的分辨率,是一种高分辨率低耗散低扩散格式.用单个守恒律方程带间断和多极值的初值问题和一维激波管问题进行了验证,比较了Harten-TVD,人工压缩,导数人工压缩方法的在间断和极值点的分辨率问题.     

波带片衍射成像技术在对地观测卫星中的应用

介绍了国外波带片衍射成像技术在对地观测领域中的发展,论述了光子筛技术的演化历程,并总结了衍射成像技术在国外高轨和低轨卫星成像中的应用,提出了波带片衍射成像技术可使“低轨成像卫星小型化”和“高轨成像卫星高分（辨率）化”的优势,及目前国外在研究波带片衍射成像技术中存在的带宽较窄、薄膜折叠展开方法、衍射透镜面形保持等问题,并对中国开展用于对地观测的波带片衍射成像技术的研发提出了建议。     

一种用于CT空间分辨率增强的图像重建方法

为在既有硬件条件下提高工业计算机层析成像(computed tomography,CT)系统空间分辨率,分析了提高采样频率的半像素错位工业CT扫描方法,提出了一种基于代数迭代技术的直接重建算法,以面积权值对采集到的投影数据进行交替迭代,实现高分辨率图像重建.利用星型空间分辨率模型开展了计算机仿真分析,重建图像截止频率处的调制度达到0.8,表明了该方法提高空间分辨率的潜力.标准空间分辨率测试卡工业CT扫描重建实验结果进一步验证了其可行性和有效性.该方法简单、易行,在工业CT系统中有良好工程应用前景.      

深空组合导航中天文测速观测研究

深空天文测速导航方法以空间中的某颗恒星为目标,利用航天器自身携带的光谱仪测量相对于恒星的移动速度来实时调整自行速度和路线。太阳是主要的测速导航源之一,利用目前运行的空间卫星的光谱观测资料,分析和研究了太阳相对于卫星的视线速度和速度误差变化情况,为本项目中自主导航光谱提供实测证据。选取了太阳表面5个位置的光谱观测,持续时间在一个小时左右,通过高斯谱线轮廓拟合观测数据,得到了太阳表面5个位置的亮度变化、谱线宽度和速度,其中主要参数速度平均值大约在10 km/s,和速度变化在3 km/s。这是由太阳表面存在大量的微观尺度上的物质运动所导致的。     

基于压缩感知的频率和DOA联合估计算法

波达方向（DOA）估计是阵列信号处理的一个重要问题,针对信号的DOA估计问题,本文基于压缩感知理论,提出了一种新的频率和角度联合估计算法。首先利用方向波数的空间稀疏性,建立过完备稀疏字典,然后利用压缩采样阵列结构通过求解最优化问题得到方向波数的高分辨估计,最后利用最优空域滤波实现信号到达角度和频率的配对。相对于传统算法,该算法能够实现多信号DOA的高分辨估计,且经过压缩采样后降低了运算量,仿真验证了该算法的正确性与有效性。     

Ambiguity resolution performance with GPS and BeiDou for LEO formation flying

The evolving BeiDou Navigation Satellite System constellation brings new opportunities for high-precision applications. In this contribution the focus will be on one such application, namely precise and instantaneous relative navigation of a formation of LEO satellites. The aim is to assess the ambiguity resolution performance with the future GPS and BeiDou constellations depending on system choice (GPS, BeiDou, or GPS+BeiDou), single- or dual-frequency observations, receiver noise, and uncertainties in ionosphere modelling. In addition, for the GPS+BeiDou constellation it will be shown how the growing BeiDou constellation in the years to come can already bring an important performance improvement compared to the GPS-only case. The performance will be assessed based on the percentage of time that the required precision can be obtained with a partial ambiguity resolution strategy.     

Predicting atmospheric delays for rapid ambiguity resolution in precise point positioning

Integer ambiguity resolution in precise point positioning (PPP) can shorten the initialization and re-initialization time, and ambiguity-fixed PPP solutions are also more reliable and accurate than ambiguity-float PPP solutions. However, signal interruptions are unavoidable in practical applications, particularly while operating in urban areas. Such signal interruptions can cause discontinuity of carrier phase arc, which introduces new integer ambiguities. Usually it will take approximately 15 min of continuous tracking to a reasonable number of satellites to fix new integer ambiguities. In many applications, it is impractical for a PPP user to wait for such a long time for the re-initialization. In this paper, a method for rapid ambiguity fixing in PPP is developed to avoid such a long re-initialization time. Firstly, the atmospheric delays were estimated epoch by epoch from ambiguity-fixed PPP solutions before the data gap or cycle slip occurs. A random walk procedure is then applied to predict the atmospheric delays accurately over a short time span. The predicted atmospheric delays then can be used to correct the observations which suffer from signal interruptions. Finally, the new ambiguities can be fixed with a distinct WL-LX-L3 (here LX denotes either of L1, L2) cascade ambiguity resolution strategy. Comprehensive experiments have demonstrated that the proposed method and strategy can fix zero-difference integer ambiguities successfully with only a single-epoch observation immediately after a short data gap. This technique works even when all satellites are interrupted at the same time. The duration of data gap bridged by this technique could be possibly extended if a more precise atmospheric delay prediction is found or on-the-fly (OTF) technology is applied. Based on the proposed method, real-time PPP with integer ambiguity fixing becomes more feasible in practice.     

GNSS carrier phase ambiguity resolution based on integrity restriction in ambiguity domain

Carrier phase ambiguity resolution of Global Navigation Satellite System (GNSS) is a key technology for high-precision navigation and positioning, and it is a challenge for applications which require both high accuracy and high integrity. This paper proposes efficient ambiguity resolution methods based on integrity restriction using Fixed Failure rate Ratio Test (FF-RT) and Doubly Non-central F-distribution Ratio Test (DNF-RT), and derives the related processing models and numerical algorithms compared with the traditional Ratio Test (RT) method. Firstly, the integer ambiguity resolution and validation procedures, especially the Least squares AMBiguity Decorrelation Adjustment (LAMBDA) estimation and RT validation are analyzed. Then the quality evaluation using success rate, the FF-RT method using Integer Aperture (IA) estimation and the NDF-RT method are proposed. Lastly, the simulation and analysis for LAMBDA using RT, FF-RT and DNF-RT methods are performed. Simulation results show that in case of unbiased scenario FF-RT and DNF-RT have similar performances, which are significantly better than RT. In case of biased scenario it is difficult for FF-RT to predict the biased success rate thus it should not be used for bias detection, while DNF-RT can detect biases in most cases except for the biases are approximate or equal to integer, which has the important benefit for early detection of potential threat to the position solution.     

基于属性散射中心模型的SAR超分辨成像算法

基于属性散射中心模型,提出一种稳健而快速的SAR超分辨成像算法,在超分辨的同时能够考虑到目标的整体结构。首先利用改进的正交匹配追踪算法（OMP）对简化的属性散射中心模型进行参数估计,然后基于估计的模型参数在信号重构时进行二维频谱外推实现SAR超分辨成像。该算法具有较高的运算效率,相对于传统基于点散射模型超分辨算法,能够有效解决部件不连续的问题,并且初始指向角的利用可以取得良好的聚焦效果。仿真实验验证了该算法的优越性。     

基于稀疏重构的杂波环境下红外图像空间邻近目标超分辨方法

基于稀疏重构的超分辨方法是应对空间邻近目标的有效方法之一,但是当目标处于杂波环境下时,杂波会布满在整个视场范围内,导致场景原有的稀疏性被破坏。针对这一现象提出了一种在杂波环境下的超分辨方法。该方法充分利用了传感器的结构特性以及重构算法中的参数,通过建立观测信号的红外成像模型并利用像元网格划分的方式,建立空间邻近目标群的位置和幅度信号的稀疏表示,并利用其光学系统的点扩散函数来构造超完备字典,最后通过控制重构场景中非零元素的个数比例来使重构参数处于一个合理的区间范围,以此来达到去除杂波干扰并准确重建稀疏目标的目的。