压缩感知理论是一种充分利用信号稀疏性或者可压缩性的伞新的信号采样理论。该理论表明，通过采集少 量的信号值就可实现稀疏或可压缩信号的精确重建。该文在研究和总结已有重建算法的基础上，提出了一种新的基 于正则化的自适应匹配追踪算法(Regularized Adaptive Matching Pursuit，RAMP)用于压缩感知信号的重建。

压缩感知包含许多种的重构算法，这个文件中介绍的是基于子空间追踪算法的重构算法，可以用于ofdm系统等等

利用追踪算法标注数据的工具，安装需要matlab依赖，安装时会提示。

Path tracing is one of several techniques to render photorealistic im- ages by simulating the physics of light propagation within a scene. The roots of path tracing are outside of computer graphics, in the Monte Carlo simulations developed for neutron transport. A great strength of path tracing is that it is conceptually, mathematically, and often-times algorithmically simple and elegant, yet it is very general. Until recently, however, brute-force path tracing techniques were simply too noisy and slow to be practical for movie production rendering. They therefore re- ceived little usage outside of academia, except perhaps to generate an occasional reference image to validate the correctness of other (faster but less general) rendering algorithms. The last ten years have seen a dramatic shift in this balance, and path tracing techniques are now widely used. This shift was partially fueled by steadily increasing com- putational power and memory, but also by significant improvements in sampling, rendering, and denoising techniques. In this survey, we pro- vide an overview of path tracing and highlight important milestones in its development that have led to it becoming the preferred movie rendering technique today.

一种利用MATLAB实现的射线追踪算法，估计各种无线系统参数。

基于GPU加速的磁共振血管造影图像的并行分割与追踪算法.pdf

正交匹配追踪算法（OMP）是一种贪婪的压缩感知恢复算法，它在每次迭代中选择感知矩阵的最佳拟合列。 然后在由所有先前选择的列跨越的子空间中执行最小二乘 (LS) 优化。 这种方法不如 Basis 追踪算法准确，但计算复杂度较低。 Matlab 函数具有三个输入：稀疏度 K、测量向量 y 和传感矩阵 A。该函数的输出是恢复的稀疏向量 x。

针对电能质量中的复合扰动信号分析问题，提出一种粒子群优化(PSO)和匹配追踪(MP)算法相结合的分层搜索的原子分解方法。首先应用MP算法提取基波分量，对于去除基波分量的残差信号，利用快速傅里叶变换找寻能量最大的频率成分，采用PSO算法粗搜索出最佳匹配粒子，然后以最佳匹配粒子为中心，在一定范围内重新离散化，生成小规模原子库，再应用MP算法有针对性地进行细搜索，最终得到最佳匹配原子，提取出电能质量复合扰动特征参数。仿真结果表明，该方法能克服MP算法匹配时间长、计算量大及PSO优化MP算法残差积累过大、容易陷入局部最优、匹配参数不准确等缺点，且具有一定的抗噪性和实时性。

这是一篇描述机动目标追踪（alpha-beta算法）的论文，有些参考价值

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