经典统计信号处理教材

DSP; Introduction to singla proessing; tutorial

DSP: Introduction to singal processing

Digital Signal Processing with Matlab Examples, Volume 3, Model-Based Actions and Sparse Representation

2D_3D_PolarFourierTransform：论文“用于极坐标和球面网格的离散傅立叶变换的精确快速计算”的C ++，CUDA和MATLAB代码

自适应信号处理，包括相关算法和对应原理的解释。值得一看的好书。

Understanding the Basis of the Kalman Filter中文版数字信号处理-朱光明译，写的信号处理方面的写的非常好，适合基础学习。

Adaptive filters play a very important role in most of today’s signal processing and control applications as most real-world signals require processing under conditions that are difficult to specify a priori. They have been successfully applied in such diverse fields as communications, control, radar, and biomedical engineering, among others. The field of classical adaptive filtering is now well established and a number of key references—a widely used one being the book Adaptive Filter Theory by Simon Haykin—provide a comprehensive treatment of the theory and applications of adaptive filtering. A number of recent developments in the field, however, have demonstrated how significant performance gains could be achieved beyond those obtained using the standard adaptive filtering approaches. To this end, those recent developments have propelled us to think in terms of a new generation of adaptive signal processing algorithms. As data now come in a multitude of forms originating from different applications and environments, we now have to account for the characteristics of real life data: † Non-Gaussianity; † Noncircularity; † Nonstationarity; and † Nonlinearity. Such data would typically exhibit a rich underlying structure and demand the development of new tools, hence, the writing of this new book.

Python内核自适应过滤：在Python中实现LMS，RLS，KLMS和KRLS过滤器

MIMO Radar Signal Processing