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Digital Control Systems, Troy Nagle, Matlab Codes

WAITINPUT Prompt for user input, but no longer than t seconds

一个用matlab编写的pls的工具箱实现模式分别和预测，仅供参考(A using matlab pls toolbox to achieve pattern and prediction, respectively, for reference only)

机载SAR合成孔径雷达的地面运动目标的检测关键技术研究，包含硕士研究论文一篇(Research on key technologies to detect ground moving target airborne synthetic aperture radar SAR, including a master' s research paper)

说明：  matlab实现dfig并网，亲测可以运行出结果。(MATLAB realizes DFIG grid connection, and pro-test can run the results.)

fft离散傅立叶变化，广泛应用数字信号处理中。(Discrete Fourier fft change, the wider use of digital signal processing.)

matlab 基本程序集合，包含多个短小的程序，用于实现一些基本数学功能，如查找独立集合(basic matlab source codes)

提出了一种新的宽带DOA估计方法:频域子空间正交性测试方法(TOFS:Test of orthogonality of frequency sub- space)。该方法通过同时测试频域信号共同带宽内各频段噪声子空间与阵列流形之间的正交性来进行DOA估计。与宽带相干信号子空间方法不同,TOFS方法不需要任何初始值的预估及聚焦操作。与宽带非相干信号子空间方法也不同,TOFS方法同时测试各频段噪声子空间与阵列流形之间的正交性。本文仿真了TOFS与IMUSIC、CSSM、TOPS的性能比较。仿真结果表明TOFS方法在中等信噪比以上时有较好的性能,且避免了TOPS方法中常出现的伪峰。 (Proposed a new broadband DOA estimation methods: subspace orthogonal frequency domain test methods (TOFS: Test of orthogonality of frequency sub-space). The method by simultaneously testing the frequency domain signal bandwidth of each band noise common sub-orthogonality between space and the array manifold to be DOA estimates. And broadband coherent signal subspace different methods, TOFS method does not require any initial estimate and focusing operation values. And broadband incoherent signal subspace methods are different, TOFS method for simultaneous testing of each band orthogonal noise subspace and the array manifold between. This paper compares the performance TOFS simulation and IMUSIC, CSSM, TOPS is. Simulation results show that the method TOFS better performance when more moderate SNR and spurious peaks TOPS method avoids the often occur.)

Another adaptive kalman filter algorithm which used in the SINS platform

采用神经网络实现手写识别的一种方法，建立Bp神经网络，采用快速训练方法，可快速完成一类相关手写字体的模式识别，识别率较高，当字体变化较大识别率降低时，可重新训练具有较强的适应性。实验证实本方法较好实现了手写字符识别，但也存在识别速度较慢，有时训练不收敛等缺点(Handwriting recognition using neural network is a way to establish Bp neural network, using fast training methods, and can be related to the completion of a class of handwriting pattern recognition, recognition rate higher recognition rate when the font changed greatly reduced, they can re- Training has strong adaptability. Experiments confirmed that this method achieved a better handwritten character recognition, but there are also identified slow, and sometimes non-convergence of the training deficiencies)