一种信道系数服从高斯分布的8PSK信号信噪比盲估计算法

信噪比（SNR,Signalto Noise Ratio）是衡量通信系统性能的重要指标。针对传统信噪比估计算法要求利用训练序列或一帧数据范周内信道系数恒定等问题,提出了一种8PSK信号在信道系数服从高斯分布的衰落信道下肯信噪比估计算法。该算法取接收信号实部或虚部数值运算构成中间观测量,理论分析8PSK信号经过衰落信道后...     

∑-△型模/数转换方式的研究

模/数转换是控制系统中重要的接口电路,本文对种类繁多的模/数转换电路按照它们的转换方式进行了分类和比较,介绍了模/数转换技术的发展趋势和新兴的∑一△型模/数转换的几个基本概念.     

TDICCD相机的信噪比的研究

文章讨论了工程适用的信噪比S/N的定义、计算公式及信噪比的阈值,列举了国外典型TDICCD相机的信噪比值,最后提出TDICCD相机设计所需的信噪比值。     

群时延特性对卫星高速数传中继系统的影响

主要关注线性和抛物线特性的群时延对高速数传系统的影响,在分析了群时延特性的概念后,设计了具有仿真要求的群时延滤波器,建立模型对系统进行了仿真,得出了线性和抛物线特性的群时延对系统接收端信噪比恶化的影响,为整个系统的均衡程度提出了指标要求和参考。     

闪电成像仪的实验室定标

闪电成像仪是对地静止的天底成像遥感器,被优化适于在白天和夜间探测和定位闪电。实验室定标是发射前的业务,其作用在于保证遥感器在轨运行的性能。文章着重讨论实验室性能定标。     

空间目标可见光相机探测能力理论计算方法研究

空间目标可见光相机的探测能力主要与相机自身收集目标信号的能力、相机本身的背景噪声以及探测器组件的噪声水平有关。衡量空间目标可见光相机探测能力的主要技术指标是信噪比。在国内外相关资料比较缺乏的情况下，文章通过原理研究和理论推导，得到了空间目标可见光相机探测能力理论计算公式，并用实例进行了例证，表明该方法正确可取，为空间目标可见光相机设计时对探测能力的分析和预估提供了理论计算方法。     

空间目标可见光相机探测能力理论计算方法研究

空间目标可见光相机的探测能力主要与相机自身收集目标信号的能力、相机本身的背景噪声以及探测器组件的噪声水平有关。衡量空间目标可见光相机探测能力的主要技术指标是信噪比。在国内外相关资料比较缺乏的情况下,文章通过原理研究和理论推导,得到了空间目标可见光相机探测能力理论计算公式,并用实例进行了例证,表明该方法正确可取,为空间目标可见光相机设计时对探测能力的分析和预估提供了理论计算方法。     

TDICCD相机相对孔径影响因素的研究

文章全面分析了TDICCD相机相对孔径选择的几个因素，与近年通常只考虑地面分辨率、MTF、S／N和像移的影响，较少涉及图像混叠对像质影响不同，文章重点提出混叠对D／f选择的影响，并计算得出理想条件下遥感系统参数的最佳值。     

Tide variation monitoring based improved GNSS-MR by empirical mode decomposition

Global Navigation Satellite System multipath reflectometry (GNSS-MR) technology has great potential for monitoring tide level changes. GNSS-MR techniques usually extract signal-to-noise ratio (SNR) residual sequences using quadratic polynomials; however, such algorithms are affected considerably by satellite elevation angles. To improve the stability and accuracy of an SNR residual sequence, this study proposed an SNR signal decomposition method based on empirical mode decomposition (EMD). First, the SNR signal is decomposed by EMD, following which the SNR residual sequence is obtained by combining the corresponding intrinsic mode function with the frequency range of the coherent signal. Second, the Lomb–Scargle spectrum is analyzed to obtain the frequency of the SNR residual sequence. Finally, the SNR frequency is converted into the tide height. To verify the validity of the SNR residual sequence obtained by EMD, the algorithm performance was assessed using multigroup satellite elevation angle intervals with measured data from two station, SC02 in the United States and RSBY in Australia. Experimental results demonstrated that the accuracy of the improved algorithm was improved in the low-elevation range. The improved algorithm demonstrated high monitoring accuracy, and the effective number was not less than 80% of the total in SC02, which means it could effectively describe the trend of the tide with accuracy of approximately 10 cm, meanwhile, the RMS error of RSBY could be reduced by 30 cm, to the maximum extent. The EMD method effectively expands the range of available GNSS-MR elevations, avoids the loss of effective information, enhances considerably the utilization rate of GNSS data, and improves the accuracy of GNSS-MR tide level monitoring.     

Accuracy analysis of GNSS-IR snow depth inversion algorithms

In recent years, with the continuous development of Global Navigation Satellite System (GNSS), it has been applied not only to navigation and positioning, but also to Earth surface environment monitoring. At present, when performing GNSS-IR (GNSS Interferometric Reflectometry) snow depth inversion, Lomb-Scargle Periodogram (LSP) spectrum analysis is mainly used to calculate the vertical height from the antenna phase center to the reflection surface. However, it has the problem of low identification of power spectrum analysis, which may lead to frequency leakage. Therefore, Fast Fourier Transform (FFT) spectrum analysis and Nonlinear Least Square Fitting (NLSF) are introduced to calculate the vertical height in this paper. The GNSS-IR snow depth inversion experiment is carried out by using the observation data of P351 station in PBO (Plate Boundary Observatory) network of the United States from 2013 to 2016. Three algorithms are used to invert the snow depth and compared with the actual snow depth provided by the station 490 in the SNOTEL network. The observations data of L1 and L2 bands are respectively used to find the optimal combination between different algorithms further to improve the accuracy of GNSS-IR snow depth inversion. For L1 band, different snow depths correspond to different optimal algorithms. When the snow depth is less than 0.8 m, the inversion accuracy of NLSF algorithm is the highest. When the snow depth is greater than 0.8 m, the inversion accuracy of FFT algorithm is higher. Therefore, according to the different snow depth, a combined algorithm of NLSF + FFT is proposed for GNSS-IR snow depth inversion. Compared with the traditional LSP algorithm, the inversion accuracy of the combined algorithm is improved by 10%. For L2 band data, the results show that the accuracy of snow depth inversion of various algorithms do not change with the variations of snow depth. Among the three single algorithms, the inversion accuracy of FFT algorithm is better than that of LSP and NLSF algorithms.