MATLAB_CALLS_F77

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用MATLAB在产生的观测数据中捕获其中PRN 号码为“5”C/A 码。 （1） 观测数据应该有一定的多普勒频偏D f（-10kHz〈D f〈+10kHz）； （2） 加入高斯白噪声，使其信噪比为-20dB； （3） 捕获成功后，应给出捕获标志，并给出估计的频偏和码偏值。(Generated by MATLAB in which observed data capture PRN number is " 5" C/A code. (1) there should be some observations of Doppler shift D f (-10kHz < D f < +10 kHz) (2) by adding Gaussian white noise, so SNR is-20dB (3) capture success After the capture flag to be given, and given the estimated frequency offset and code bias values.)

用于估计谐振状态问题，比较Schmidt卡尔曼和标准卡尔曼滤波(Compares Schmidt-Kalman filter vs Kalman filter on problem of estimating the state of a damped harmonic resonator excited by white noise, and using measurements of resonator dispalcement corrupted by white noise plus exponentially correlated noise.)

自学Matlab必备的100个小程序代码，详细的对各个基础进行了编程，对初学者有很好的而帮助(The necessary 10 self-study Matlab small program code, detailed to each basis programming, for beginners is good but help )

这是一个用matlab编写的计算模型，用于分析悬索桥梁地震响应的Benchmark控制问题。(This computational model contains the original files for the Phase I Benchmark Control Problem for Seismic Response of Cable-Stayed Bridges. )

实现SPHIT编码，有n=5~2时的编码输出值！！(SPHIT coding show the output with n =5.....2)

说明：  一些matlab的程序，如交互多模，CA,CV,Singer模型的一些仿真仅供参考。(Some matlab procedures, such as interactive multimode, CA,CV,Singer model of some simulation is for reference only.)

LMS: Least mean squares (LMS) algorithms is a type of adaptive filter used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean squares of the error signal (difference between the desired and the actual signal)

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说明：  在MATLAB上所使用的PCA程序，主要應用於過濾相對較不重要的特徵值(dimension)，例如在三度空間的某些點具有(x,y,z)值，因為這些點有共同的一個持徵，就是z值相對於x,y值來得小很多(不明顯)，所以就以X,Y軸來表示這些點。此時就達成去除掉z的特徵值(dimension)。 (MATLAB used in the PCA procedure, mainly used in filtering relatively unimportant eigenvalue (dimension), for example, in some points with three-dimensional (x, y, z) values, because these points have a common one sign holders, that is, z values compared with the x, y values much smaller than (not obvious), so on to X, Y axis to express these points. Reached at this time to get rid of z eigenvalues (dimension).)