yuanzhu_zhengfang_XY

这是初学真学习SINS的好程序，里面有较详细的注释，方便读懂程序。(This is a good beginner really learn SINS program, which has more detailed notes, easy to read program.)

说明：  BLDCM的simulink模型，电流、转速双闭环控制的无刷直流电动机系统，电机模块是自己封装而成的不是用的永磁同步电动机或者自带的BLDCM，所涉及的三个程序也有展示。(The model of BLDCM, the BLDCM system with double closed-loop control of current and speed,and the motor module is a self packaged permanent magnet synchronous motor or BLDCM, and the three programs involved are also shown.)

MATLAB环境下采用灰度变换的方法增强图像的对比度(Using gray-scale transformation method of contrast-enhanced images)

BPSK基于平方谱和四次方谱的载频估计，可以用于BPSK 16QAM MSK信号的载频估计。(BPSK carrier frequency estimation based on square spectrum and fourth power spectrum, can be used for 16 qam MSK BPSK signal carrier frequency estimation)

matlab实现ofdm的一些子程序模块，可以哦(ofdm matlab to achieve some of subroutine modules, can be oh)

this is implementation huffman algorithm,you must try code. i think you must try.

Bar-Shalom-Campo融合算法( Bar-Shalom-Campo data fusion algorithm)

说明：  梯度下降法是一個一階最佳化算法，通常也稱為最速下降法。 要使用梯度下降法找到一個函數的局部極小值，必須向函數上當前點對應梯度（或者是近似梯度）的反方向的規定步長距離點進行疊代搜索。如果相反地向梯度正方向疊代進行搜索，則會接近函數的局部極大值點；這個過程則被稱為梯度上升法。(The gradient descent method is a first-order optimization algorithm, also commonly referred to as the steepest descent method. To find the local minimum of a function using the gradient descent method, an iterative search must be performed to the specified step distance point in the opposite direction of the gradient (or approximate gradient) of the current point on the function. If the search is reversed in the opposite direction to the positive direction of the gradient, it will approach the local maximum point of the function; this process is called the gradient ascent method.)

等间距线性阵列，阵元数M=8，阵元间距d= λ /2，等幅加权，目标方位φr =0°。产生空间白噪声矢量（复高斯分布）。 设阵列信号矢量元素s(m)=exp[j mψr]，其信号幅度为1，该阵元接收机附加的高斯噪声为n(m)=nmr+jnmi，其中实虚部均为独立同分布N(0, σ2)的高斯随机数，则该通道合成信号x(m)=s(m) + n(m)，其中信噪比为： SNR = 10 lg[1/(2σ2)] = – 3 – 10 lg(σ2) (dB) (The spacing of the linear array, and the array element number M = 8, the array element spacing d = λ/2, equal amplitude weighted target azimuth φR = 0. Space generated white noise vector (complex Gaussian distribution). Let array signal vector elements s (m) = exp [j mψr], the signal amplitude, the element receiver additional Gaussian noise n (m) = nmr+jnmi which the real and imaginary parts are independent and identically distributed N ( Gaussian random numbers of 0, σ2), the channel the synthesis signal x (m) = s (m) the PPLS n (m), where SNR is: SNR = 10 lg [1/(2σ2)] =- 3- 10 lg (σ2) (dB))

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