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Research-on-Compressed-Sensing
- 经典的香农采样定理认为,为了不失真地恢复模拟信号,采样频率应该不小于奈奎斯特频率(即模拟信号 频谱中的最高频率)的两倍.但是其中除了利用到信号是有限带宽的假设外,没利用任何的其它先验信息.采集到 的数据存在很大程度的冗余.Donoho等人提出的压缩感知方法(Compressed Sensing或Compressive Sampling, CS)充分运用了大部分信号在预知的一组基上可以稀疏表示这一先验信息,利用随机投影实现了在远低于奈奎斯 特频率的采样频率下对压缩数据的直接采集.该
Sparsity-Inducing-DOA
- 基于稀疏分解的宽带信号DOA估计方法，使用了基于贝叶斯的方法具有良好的估计精度和分辨率-Wideband signal sparse decomposition DOA estimation method based on the use of a method based on Bayesian estimation has good accuracy and resolution
SVR
- 基于支持向量机的短波信号盲均衡算法，该算法稀疏性好，性能稳定。-SVM shortwave signal blind equalization algorithm based on the algorithm sparsity, and stable performance.
Structured-Compressive-Sensing
- 本文围绕压缩感知的三个基本问题, 从结构化测量方法、结构化稀疏表示和结构化信号重构三个方面对结构化压缩感知的基本模型和关键技术进行详细的阐述.-In this paper, the basic models and key techniques of structured compressive sensing are introduced in terms of the structured measurements, the structured dictionary representat
Underdetermined-DOA-Estimation
- 比较新的稀疏重构宽带DOA方法，供学习阵列信号处理的参考-Relatively new broadband DOA sparse reconstruction method for learning the reference array signal processing
dissert
- 本文档是Karl Skretting的博士论文，主要研究dictionary learning，以及重叠帧里稀疏信号的呈现。-This document is Karl Skretting s PhD thesis, which focuses on dictionary learning and the presentation of sparse signals in overlapping frames.
l1magic
- 实现压缩感知的稀疏信号恢复，采用L1范数约束最小化策略(Sparse signal recovery with compressed sensing, by using the L1 norm constraint minimization strategy)