DML与SML算法求解目标声源波达方位估计DOA的仿真程序

The GNSS integer ambiguities_ estimation and validation

fcm代码matlab 面部图像的ML年龄估计 在Matlab中构建的代码使用的数据集是FGNET的。必须先编译并运行名为preprocessing.m的文件，然后编译并运行名为Feature Extraction的文件，以便我们将具有所有特征值的.mat文件用于聚类名为fcm_age的零件文件进行测试和估算根据模糊c均值聚类算法计算年龄。 使用SVM分类器对> 30岁和<30岁的二元年龄组进行分类。

matlab中分式的代码非线性离散时间分数系统的状态估计A-贝叶斯视角 论文源代码 STBL Alpha 稳定分发文件夹 使用了 Mark Veillette 编写的 MATLAB 函数库“STBL”。 源代码可以在以下位置下载： 关于该函数库的详细说明，请参见： SISO_Gau_FPF 分数高斯系统的 FPF：标量情况。 FourAlgCompare 文件夹 SISO 分数高斯系统的 FEKF、FCDKF、FUKF 和 FPF 的比较。 MIMO_Gau_FPF 分数高斯系统的 FPF：多元情况。 SISO_NonGau_FPF 文件夹 分数非高斯系统的 FPF @article{liu2019state, title={State estimation for nonlinear discrete--time fractional systems: A Bayesian perspective}, author={Liu, Tianyu and Wei, Yiheng and Yin, Weidi and Wang, Yong and Liang, Qing},\njournal=

Video-Compression-motion-estimation-block-video-encoder：此存储库与视频压缩有关，更具体地说，与视频编码器的运动估计块（ME块）有关。 这是一个研究项目，旨在开发一种有效的运动估计算法，从而使视频压缩技术能够与高帧率视频和高分辨率视频保持同步。

双选信道的各种方法，有常见的一些LS，MMSE和OMP算法，还有一张对比图

State Estimation for Robotics_简介-附件资源

DINA模型及其参数估计，作者Jimmy de la Torre，发表于2009年，引用数为：385

This textbook evolved from a course in geophysical inverse methods taught during the past two decades at New Mexico Tech, first by Rick Aster and, subsequently, jointly between Rick Aster and Brian Borchers. The audience for the course has included a broad range of first- or second-year graduate students (and occasionally advanced under- graduates) from geophysics, hydrology, mathematics, astrophysics, and other disciplines. Cliff Thurber joined this collaboration during the production of the first edition and has taught a similar course at the University of Wisconsin-Madison. Our principal goal for this text is to promote fundamental understanding of param- eter estimation and inverse problem philosophy and methodology, specifically regarding such key issues as uncertainty, ill-posedness, regularization, bias, and resolution. We emphasize theoretical points with illustrative examples, and MATLAB codes that imple- ment these examples are provided on a companion website. Throughout the examples and exercises, a web icon indicates that there is additional material on the website. Exercises include a mix of applied and theoretical problems. This book has necessarily had to distill a tremendous body of mathematics and science going back to (at least) Newton and Gauss. We hope that it will continue to find a broad audience of students and professionals interested in the general problem of estimating physical models from data. Because this is an introductory text surveying a very broad field, we have not been able to go into great depth. However, each chapter has a “notes and further reading” section to help guide the reader to further explo- ration of specific topics. Where appropriate, we have also directly referenced research contributions to the field. Some advanced topics have been deliberately left out of this book because of space limitations and/or because we expect that many readers would not be sufficiently famil- iar with the required mathematics. For example, readers with a strong mathematical background may be surprised that we primarily consider inverse problems with discrete data and discretized models. By doing this we avoid much of the technical complexity of functional analysis. Some advanced applications and topics that we have omitted include inverse scattering problems, seismic diffraction tomography, wavelets, data assimilation, simulated annealing, and expectation maximization methods. We expect that readers of this book will have prior familiarity with calculus, dif- ferential equations, linear algebra, probability, and statistics at the undergraduate level. In our experience, many students can benefit from at least a review of these topics, and we commonly spend the first two to three weeks of the course reviewing material from

We live in a new age for statistical inference, where modern scientific technology such as microarrays and fMRI machines routinely produce thousands and sometimes millions of parallel data sets, each with its own estimation or testing problem. Doing thousands of problems at once is more than repeated application of classical methods. Taking an empirical Bayes approach, Bradley Efron, inventor of the bootstrap, shows how information accrues across problems in a way that combines Bayesian and