Simpson

说明：  此程序实现自适应波束图中的不同的阵列间距和期望信号方向对波束图的影响(Realize this procedure adaptive beamforming map different array spacing and the desired signal direction of the impact of beamforming map)

本例程对十字形32QAM信号进行盲均衡处理，码间干扰可以降低到-30db，输出星座图更清晰(The routines of cross blind equalization 32QAM signal processing, inter-symbol interference can be reduced to-30db, output constellation diagram more clearly)

考虑L的三个不同值：L＝256（3个数据段），L＝128(7个数据段)和L=64（15个数据段）。各自的谱估计图如上图所示。可以明显的看到，加窗明显的减小了频谱上的假谱峰，但也更加进一步平滑了谱峰。所以，对于L＝64的情况，在ω＝0.8π的谱线可以很确定的辨认，但是那两个靠近的谱峰不容易区分。对于L＝128的情况，这种情况提供了在分离和检测间最好的均衡。当然，对于在L＝256时的情况，效果是更好的，能够从谱估计图上明显的分辨出三条谱线的存在以及它们幅度关系的强弱。 除了Welch法外，还可以采用对多个周期图求平均的功率谱估计方法的其他方法如Bartlett法等等，在功率谱估计上也能取得较好的结果。(consider three different values : L = 256 (3 data), L = 128 (7 data) and L = 64 (15 data). Their spectral estimation map as shown in Fig. It is clear to see that increase significantly decreasing window of the spectrum of false peaks, but much more to further smooth the peaks. Therefore, L = 64, in the case = 0.8 spectrum can determine the identification, but that the two peaks around is not easy to distinguish. L = 128 for the situation, which provided the separation and detection among the best balanced. Of course, L = 256 in the case, the effect is better, from the spectral estimation map clearly distinguish between the three lines of the existence and their relationship to the rate of strength. In addition to Welch law, but also can be used to map multiple cycle for the average power)

分数阶傅里叶变换的matlab程序，做数字信号处理可以直接使用(Fractional Fourier transform matlab program)

功率谱分析和matlab仿真 周期性分析(Power spectrum analysis and Matlab simulation )

这是一个测试gsvd.m函数的matlab代码(This is a test gsvd.m function Matlab code)

切比雪夫最佳一致逼近设计多带线性相位FIR滤波器(Chebyshev best uniform approximation design multi-band linear phase FIR filter)

正交最小二乘辨识算法 该算法除了实现最小二乘辨识功能之外而且能按照各项重要性将其逐一选出并且估计相应系数(OLS Orthogonal Least Quares. [x, ind] = OLS(A,b,r) gives the solution to the least squares problem using only the best r regressors chosen from the ones present in matrix A. This function also returns in the vector ind the indexes of the best r regressors (i.e., the best columns of A to use). If r is equal to n, the solution x given by OLS is the same as the solution given by A, but in ind we still have the regressors sorted by their importance. This means that one can perform a feature selection by taking the first k entries in ind (k<r).)

…………………………粒子滤波程序…………………………(.............................. Particle filter procedure ..............................)

simulink自适应噪声信号2，Matlab中的s函数(simulink adaptive noise signal 2, Matlab function of s)