8

proyecto de sistema de informacion

自收发光电检测，发射时间维持5ms左右，从PORT5读入光电接收管状态，不断进行自收发光电信号检测(zi receive light and )

参照CCSDS标准的RS编码 交织深度参照CCSDS标准(Refer to the RSS standard of the CCSDS standardInterweaving depth refers to the CCSDS standard)

concttuc one matrix using matlab

坐标转换（四参数） [row col]=size(source) for i=1:row u(i)=source(i,2) v(i)=source(i,3) x(i)=source(i,4) y(i)=source(i,5) B(2*i-1,1)=1 B(2*i-1,2)=0 B(2*i-1,3)=u(i) B(2*i-1,4)=-v(i) B(2*i,1)=0 B(2*i,2)=1 B(2*i,3)=v(i) B(2*i,4)=u(i) L(2*i-1,1)=x(i) L(2*i,1)=y(i) end X=inv(B *B)*(B *L) Txy=[X(1) X(2)] xzjz(1,1)=X(3) xzjz(1,2)=-X(4) xzjz(2,1)=X(4) xzjz(2,2)=X(3) [row1 col1]=size(target) for i=1:row1 u1(i)=target(i,2) v1(i)=target(i,3) d=[u1(i) v1(i)] md=Txy+xzjz*d end end (Coordinate conversion： [row col]=size(source) for i=1:row u(i)=source(i,2) v(i)=source(i,3) x(i)=source(i,4) y(i)=source(i,5) B(2*i-1,1)=1 B(2*i-1,2)=0 B(2*i-1,3)=u(i) B(2*i-1,4)=-v(i) B(2*i,1)=0 B(2*i,2)=1 B(2*i,3)=v(i) B(2*i,4)=u(i) L(2*i-1,1)=x(i) L(2*i,1)=y(i) end X=inv(B *B)*(B *L) Txy=[X(1) X(2)] xzjz(1,1)=X(3) xzjz(1,2)=-X(4) xzjz(2,1)=X(4) xzjz(2,2)=X(3) [row1 col1]=size(target) for i=1:row1 u1(i)=target(i,2) v1(i)=target(i,3) d=[u1(i) v1(i)] md=Txy+xzjz*d end end )

这个包，包含了好多比较经典的MATLAB程序 这是本人收藏多年的成果，现在上传给大家分享(This package contains a lot of comparison classic MATLAB program which is the outcome of my collection for many years, and now upload to the U.S. to share)

1 swing bus、1 PQ bus、2 transfermor 以matlab劃出結構圖後計算各線路的電流電壓(1 swing bus, 1 PQ bus, 2 transfermor matlab draw diagram after calculation of current and voltage of each line)

MIMO-OFDM系统子空间法进行盲信道估计，可更改循环前缀长度以比较不同CP对估计性能的影响。(subspace method for blind channel estimation for MIMO-OFDM system. One can change the length of cyclic prefix in order to compare the performance of different CP impact on the estimates.)

级联H桥三相七电平matlab仿真模型，分享供大家使用(Cascade H-bridge three-phase seven-level Matlab simulation model to share for everyone to use)

Synthetic aperture radar imaging provides high resolution images of large areas. The intensities of pixels in a SAR image are based on the spatial orientation, roughness, and dielectric constant of the surface imaged. SAR is an active sensor, transmitting its own energy, and then measuring the return scattered by the earth抯 surface back to the satellite抯 antenna. SAR processing is the transformation of raw SAR signal data into a spatial image. In this communication, we present a contribution concerning implementation of a SAR image formation with the IDL language. This process is performed in a frequency domain correlation of the received signal including range compression, range migration, and azimuth compression. The output is the final step in which the data is transposed back into its original format and written to an output file.