kam1

负正弦加白噪声随机过程下使用MUSIC方法进行信号频率估计仿真，信号样本数去1000，估计的自相关矩阵为8阶，分别采用AIC和MDL准则估计信号源个数，并画出相应的MUSIC频率估计谱线。(Negative sine plus white noise random process method using MUSIC frequency estimation simulation, signal samples to 1000, the estimated autocorrelation matrix of order 8, AIC and MDL criteria were used to estimate the number of signal sources, and draw the corresponding MUSIC frequency spectrum estimation.)

说明：  matlab中增减采样以及样条差值的程序实例，为读者提供初等帮助。(matlab to increase or decrease the difference between sampling and spline examples of procedures that provide readers with elementary help.)

无人驾车车轨迹跟踪控制，是目前社会研究的重点。本程序代码实现matlab联合carsim进行联合仿真，实现追踪给定轨迹。经测试，在低速状态，追踪效果很理想。(Unmanned driving vehicle trajectory tracking control, is currently the focus of social research.This procedure code realization of matlab joint carsim joint simulation, track the given trajectory.After the test, in low speed condition, tracking effect is very ideal.)

现代谱估计welch方法，可以变换成巴特莱特方法(welch altroithm in modern spectral estimation)

说明：  Randon data from program

针对双基阵提供的有偏方位角量测信息,对双基阵纯方位目标可观测性的必要条件及其Cramer-Rao下限 进行了理论推导.在此基础上,采用一种新的辅助变量方法对双基阵纯方位跟踪性能进行改进,并在可观测条件下对 目标进行了蒙特卡洛仿真实验.实验结果表明,新的辅助变量方法可以使参数估计精度大大提高,并且上述理论对制 定实际的跟踪策略或算法具有一定的参考价值 (For double-base matrix provided biased information azimuth measurement of double-base bearings-only target array may be a necessary condition for observability and Cramer-Rao lower limit of the theoretical derivation. On this basis, a new auxiliary variable method of double-base bearings-only tracking array to improve performance and can be observed under the condition of targets Monte Carlo simulation. The experimental results show that the new auxiliary variable method can greatly improve the accuracy of parameter estimation and the formulation of the above-mentioned theory tracking the actual strategy or algorithm has certain reference value)

Matlab code to detect Harris corner points in user selected area.

程序设计问题和学习有效题目，可以有效提高初学者学习能力。(it can inprove the ability of the learners)

用于对路标进行提取和识别！对于实验环境下的路标识别效果比较好！(To sign for extraction and recognition. For the test environment road signs recognition effect is better! )

说明：  经验模态分解（Empirical Mode Decomposition，简称EMD))方法被认为是2000年来以傅立叶变换为基础的线性和稳态频谱分析的一个重大突破，该方法是依据数据自身的时间尺度特征来进行信号分解，无须预先设定任何基函数。这一点与建立在先验性的谐波基函数和小波基函数上的傅里叶分解与小波分解方法具有本质性的差别。(Empirical mode decomposition (EMD) method is considered as a major breakthrough in linear and steady-state spectrum analysis based on Fourier transform since 2000. This method decomposes signals according to the time scale characteristics of the data itself, without setting any basis function in advance. This is essentially different from Fourier decomposition and wavelet decomposition based on prior harmonic basis function and wavelet basis function.)