matlabdeal

matlab 编程书籍 希望对大家有力(matlab programming books for all of us hope that a strong)

上传一个word档的联邦式扩展卡尔曼粒子滤波算法，大家学习粒子滤波有益，为了使联邦滤波器够有效处理非高斯、非线性系统的状态估计问题，提出将扩展卡尔曼粒子滤波引入联邦滤波结构中，得到一种新的联邦式扩展卡尔曼粒子滤波算法.使用扩展卡尔曼粒子滤波对联邦滤波子系统的多源数据进行处理，从而摆脱了经典卡尔曼滤波的限制，拓宽了联邦滤波器的实际应用范围.将联邦式扩展卡尔曼粒子滤波算法应用于非线性滤波器的一个标准验证模型进行了仿真实验，结果表明该算法是有效性的.(Abstract: A new particle filter(Federated Extend Kalman Particle Filter，EKF-FPF) is proposed to estimate the state of Non-Gaussian and Non-Linear system for federated filter, in which extend kalman particle filer is introduced to federated filter so that the information fusion of subsystem can be solved by the non-gaussian and non-linear filer. By doing so, the federated filter can get rid of the disadvantage of the ordinary kalman filter to extend its application field. The simulation results of the standard testing model demonstrate the feasibility of the proposed algorithm.)

约束优化问题的matlab 多个元程序(Constraints multiple element program matlab)

可自行设置参数产生SCM信道，支持MIMO等功能(can set your own parameter to create SCM channel,support MIMO an other function.)

可以对数量少、残缺、不完整的数据进行预测，如人口、缺水量等。也可以对灾害发生时间进行预测。(Can be a small number, mutilated, incomplete data to predict, such as population, water shortage and so on. You can also predict the time of occurrence of the disaster.)

说明：  一种简单的求梅尔倒谱的方法,运用该种方法能较快得到mel倒谱。(Seek a simple method of Mel cepstrum, the use of the method can be faster mel cepstrum.)

说明：  本包讲解的是关于扩展卡尔曼滤波同时定位与见图的MATLAB仿真代码(This package explains the MATLAB simulation code for simultaneous Kalman filtering simultaneous positioning and reference.)

说明：  包含了《精通MATLAB》书中所有源代码，极具参考与实用价值！(Contains the )

说明：  matlab下看地震切片的程序，配合seislab使用(matlab seismic sections look at the procedure, coupled with the use of seislab)

传统的短时谱估计语音增强算法通常假设语音谱分量相互独立，没有考虑语音谱分量间的相关性。针对这 一问题，该文提出一种新的基于多元Laplace分布模型的短时谱估计算法。首先，假设语音的离散余弦变换(DCT) 系数服从多元Laplace分布，以此利用谱分量间的相关性；在此基础上，利用多元随机矢量的高斯尺度混合模型表 示，推导得到语音DCT系数矢量的最小均方误差(MMSE)估计的解析表达式；并进一步推导了基于该分布模型的 语音存在概率，对最小均方误差估计子进行修正。实验结果表明，该算法在抑制背景噪声和减少语音失真等方面优 于传统的语音增强方法。(The spectral components of speech are usually assumed to be independent in traditional short-time spectrum estimation, which is not the case in practice. Tosolve this problem, a new speech enhancement algorithm with multivariate Laplace speech model is proposed in this paper. Firstly, the speech Discrete Cosine Transform (DCT) coefficients are modeled by a multivariate Laplace distribution, so the correlations between speech spectral components can be exploited. And then a Minimum-Mean-Square-Error (MMSE) estimator based on the proposed model is derived using a Gaussian scale mixture representation of random vectors. Furthermore, the speech presence uncertainty with the new model is derived to modify the MMSE estimator. Experimental results show that the developed method has better noise suppression performance and lower speech distortion compared to the traditional speech enhancement method. )