WTB_v3

运用拉普拉斯高斯金字塔方法进行景象匹配,实验证明该法能满足实时性和准确性.(The use of Gaussian Laplacian pyramid scene matching methods, experimental proof of the Act can satisfy the real-time performance and accuracy.)

高阶谱双谱程序，非常实用的双谱例程供你参考(Order spectrum bispectral procedures, very practical double spectral routines for your reference)

利用直线阵列麦克风接收信号，使用music算法实现声源的定位，有噪声状态下效果好。(Using a linear array microphone receives the signal, the music algorithm to achieve the positioning of the sound source, the effect of noise state.)

波束形成，多信号的信号谱估计，基于线性约束最小方差准则(Beamforming, signal spectrum multiple signal estimates based linearly constrained minimum variance criterion)

basically a trial run to get back into the mode of things. This is a simple MCM communications system. The code is pretty self-explanatory

利用matlab研究使用维纳滤波器对语音信号去噪的例程(Using matlab studies using Wiener filter speech signals denoising routines)

space coding with maxima ratio combining.

航空飞行力学基本坐标系的转换、大气模型等等！(Aviation mechanics coordinate system conversion, atmospheric models and more!)

RBF神经网络自适应控制MATLAB仿真代码(RBF neural control )

产生右图所示图像f1(m,n)，其中图像大小为256×256，中间亮条为128 ×32，暗处=0，亮处=100。对其进行FFT： ① 同屏显示原图f1(m,n)和FFT(f1)的幅度谱图； ② 若令f2(m,n)=（-1）m+n f1(m,n)，重复以上过程，比较二者幅度 谱的异同，简述理由； ③ 若将f2(m,n)顺时针旋转90 度得到f3(m,n)，试显示FFT(f3)的幅 度谱，并与FFT(f2)的幅度谱进行比较； ④ 若将f1(m,n) 顺时针旋转90 度得到f4(m,n)，令f5(m,n)=f1(m,n)+f4(m,n)，试显 示FFT(f5)的幅度谱，并指出其与FFT(f1)和FFT(f4)的关系； ⑤ 若令f6(m,n)=f2(m,n)+f3(m,n)，试显示FFT(f6)的幅度谱，并指出其与FFT(f2)和 FFT(f3)的关系，比较FFT(f6)和FFT(f5)的幅度谱。(Generating an image f1 (m, n) shown in the figure, wherein the image size is 256 256, the intermediate light bar 128 32 0 = dark, bright Department 100. Its FFT: ① screen display picture f1 (m, n) and the FFT (f1) of the amplitude spectrum ② If so f2 (m, n) = (-1) m+n f1 (m, n), repeat The above process, comparing the amplitude spectrum of the similarities and differences between the two, brief reasons ③ If f2 (m, n) 90 degrees clockwise to get f3 (m, n), try to display FFT (f3) the amplitude spectrum and with the FFT (f2) comparing the amplitude spectrum ④ If f1 (m, n) obtained by 90 degrees clockwise f4 (m, n), so f5 (m, n) = f1 (m, n)+f4 (m, n ), try to display FFT (f5) amplitude spectrum, and pointed out its relationship with the FFT (f1) and FFT (f4) of ⑤ If so f6 (m, n) = f2 (m, n)+f3 (m, n) and try to display the FFT (f6) amplitude spectrum, and pointed out its relationship with the FFT (f2) and FFT (f3), comparing FFT (f6) and FFT (f5) amplitude spectrum.)