aggregation-of-matlab-function

reverberation enhancement program

matalb建模仿真与实例精讲的源代码，希望有助于你，也希望你分享更多东西(matalb Modeling, Simulation and the source code of the instance of Jingjiang)

gilbert strang matlab Computational Science and Engineering

Algorithm Cohena-Sutherlanda

极化SAR，pauli分解San Francisco地区RGB合成图(Polarization SAR Pauli decomposition San Francisco RGB composite image)

MATLAB仿真二阶锁相环PLL，希望对大家有用(Second-order phase-locked loop)

文章关于自适应的卡尔曼的算法在变形数据处理上的应用(Application of the Calman on the adaptive algorithm in data processing of deformation on the)

说明：  摘要：简述信道编码理论，详细说明分组码的编译原理、实现方法及检错纠错能力，用MATLAB仿真有无信道编码条件下对通信系统性能的影响及信道编码在不同信道下对通信系统性能的影响，如AWGN信道和深衰落信道。 关键词：信道编码、分组码、MATLAB仿真、性能 (Abstract: This paper describes channel coding theory, the detailed block code compiler theory, implementation method, and error detection and correction capabilities, with the MATLAB simulation without channel coding conditions on the communication system performance and channel coding in the different channels of communication systems properties, such as deep fading channel and AWGN channel. Keywords: channel coding, block codes, MATLAB simulation, performance)

因而本文基于MATLAB 针对Nakagami 衰落信道仿真了它的统计 特性,包括接收信号的复包络模拟和在具有最大比合并分集下的电平通过率及平均衰落持续时间的性能分析。重点在于理解和建立采用 MATLAB 进行信道信真的方法及对Nakagami 衰落信道本身统计特性的理解。(Based on MATLAB which Nakagami fading channel simulation for its statistical properties, including the received signal complex envelope simulation and has the maximum ratio combining diversity under the level crossing rate and average fade duration of the performance analysis. Emphasis is on understanding and the establishment of the channel using MATLAB and the letter is really way Nakagami fading channel statistical characteristics of their own understanding.)

实现了1D,2D,3D空间自由度下的运动参数的扩展Kalman滤波算法，文章2008 IROS Visual SLAM for 3D Large-Scale Seabed Acquisition Employing Underwater Vehicles的具体实现。(a novel technique to align partial 3D reconstructions of the seabed acquired by a stereo camera mounted on an autonomous underwater vehicle. Vehicle localization and seabed mapping is performed simultaneously by means of an Extended Kalman Filter. Passive landmarks are detected on the images and characterized considering 2D and 3D features. Landmarks are re-observed while the robot is navigating and data association becomes easier but robust. Once the survey is completed, vehicle trajectory is smoothed by a Rauch-Tung-Striebel filter obtaining an even better alignment of the 3D views and yet a large-scale acquisition of the seabed.)