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关于主成分分析方法的主元选取方法“广义重构误差方差法”（Generalized VRE）的MATLAB仿真代码。(Principal component analysis on the primary unit selection method " generalized reconstruction error variance method" (Generalized VRE) MATLAB simulation code.)

Kongsberg EM 系列多波束解码说明书，可以参考里面的说明帮助实现底层解码。(Kongsberg EM Series Multibeam echo sounder EM datagram formats)

增广最小二乘法参数估计，系统辨识，针对含有输出噪声的SISO模型。(Augmented least squares parameter estimation, system identification, aimed at containing the output noise of the SISO model.)

canca算法 系统辨识源程序 供大家参考(canca algorithm for system identification source for reference)

利用压缩感知对雷达进行成像， 结果显示良好(Using compressed sensing to image the radar, The results are good)

线性调频信号的回波仿真，比较特别的是对近距目标的仿真(the stimulation of Linear fm signal wave , particularly to the close goals )

说明：  该算法实现了宽带麦克风阵列的波束形成，主要采用了频域DFT和时域FIR波束形成器(The algorithm for broadband microphone array beam-forming, the main frequency domain using DFT and time-domain FIR Beamformer)

MIMO雷达模型下一种子空间谱估计方法，采用过估计的方法，以避免信源数估计的问题，直接对数据协方差矩阵进行变换，从而构造了信号子空间投影矩阵和噪声子空间投影矩阵，不需要像经典的MUSIC一样对其进行特征分解，完全避开了在一般非理想情况下MUSIC算法必须面对的识别小特征值与大特征值的麻烦，降低了复杂度，而且该方法不受快拍数的影响，在相干源情况下也能准确的估计目标的入射角，不会出现伪峰。(A subspace based DOA (Direction-Of-Arrival) estimation method for MIMO (Multiple-Input Multiple-Output) radar is studied in this paper. By transforming the data covariance matrix, we can construct the projection matrix of signal subspace and noise subspace. The computation complexity of the algorithm is reduced as it does not need to perform eigendecomposition. By using the over estimated theory, this algorithm avoid estimating the number of signal sources and can keep a good estimate performance in the case of lower SNR (Signal-to-Noise Ratios) and multi-target. )

二阶耦合系统的解耦控制，前馈解耦，带smith预估补偿解决大滞后问题。(Decoupling control system coupled second order feedforward decoupling.)

灰色理论认为系统的行为现象尽管是朦胧的，数据是复杂的，但它毕竟是有序的，是有整体功能的。灰数的生成，就是从杂乱中寻找出规律。同时，灰色理论建立的是生成数据模型，不是原始数据模型，因此，灰色预测的数据是通过生成数据的gm(1,1)模型所得到的预测值的逆处理结果。(Gray theory that the behavior of the system even though the phenomenon is dim, the data is complex, but still it is ordered, there is a whole function. Gray number generation, that is, from the clutter to find out the laws. Meanwhile, the gray theory is to generate the data model, not the original data model, so gray prediction data by generating data gm (1,1) model predicted values ​ ​ obtained result of the inverse process.)