MATLAB

This file compares three different versions of the Kalman filter. The Kalman filter is used for recursive parameter estimation. The Kalman filter can handle noisy measurements. The first implemented filter (fcn_KF) is the Kalman filter with standard update of the covariance matrix P. The covariance matrix reflects the uncertainties of the predictions. To improve the numerical stability Potter developed a square root update (fcn_KF_SRP) of the covariance matrix P. Another version is the square root covariance update via triangularization (fcn_KF_SRT). This file generates a model. Then the three Kalman filters perform an estimation of the model parameter. At the end the results are compared. Sources: Simon, D. (2006): Optimal state estimation Kaminski, P. (1971): Discrete Square Root Filtering: A Survey of Current Techniques Golub, G. (1996): Matrix Computations

In this paper, we investigate the timing and carrier frequency offset (CFO) synchronization problem in decode and forward cooperative systems operating over frequency selective channels. A training sequence which consists of one orthogonal frequency-division multiplexing (OFDM) block having a tile structure in the frequency domain is proposed to perform synchronization. Timing offsets are estimated using correlation-type algorithms. By inserting some null subcarriers in the proposed tile structure, we propose a computationally efficient subspace decomposition- based algorithm for CFO estimation. The issue of optimal tile length is studied both theoretically and through simulations. By judiciously designing the tile size of the pilot, the proposed algorithms are shown to have better performance, in terms of synchronization errors and bit error rate, than the time-division multiplexing-based training method and the computationally demanding space-alternating generalized expectation-maximization

viterbi硬判决代码，用于卷积码译码(hard decision viterbi code for decoding convolutional codes)

In this paper a Conventional PD Controller and Fuzzy Neural Controller for trajectory tracking of mobile robot platform is presented. Using computed torque method is given input- torque on mobile robot wheels. The locomotion control structure is based on integration of kinematics and dynamics model of mobile robot base. The proposed Control scheme and Fuzzy Neural Algorithm could be useful for building an autonomous non-destructive testing system based on wheeled mobile robot. Fuzzy Neural Network-Rule base, is optimized using Genetic Algorithm. Simulations are done using Matlab software. Key words:Mobile Robot, Conventional, Fuzzy Logic, Neural Network, Genetic Algorithm, trajectory tracking,

matlab仿真的直接扭矩控制，模型参数没有调出来，给大家看看。(Direct torque control matlab simulation, the model parameters do not tune out for everyone to see.)

粗糙集MATLAB程序~~~~~~~~~~~~~~~~~~~~~(MATLAB PROGRAM)

此程序是个matlab程序，其算法结构为bp网络，可以处理一些优化问题(This procedure is a matlab program, its algorithm structure for the bp network that can handle a number of optimization problems)

this is a presentation that explains matlab in detail.useful for engineers

实现了OFDM系统的建模仿真，精确比较了其误差，精度等问题。通过该系统的学习有利用其它通信传输模型的设计(realise OFDM system modeling and simulating.through this system,we can easy learing other commucation systens.)

matlab车牌识别程序(带有详细的注释)(matlab license plate recognition program (with detailed comments))