1) AR Auto-Correlation Matching Pursuit (AR-MP)
AR自相关匹配追踪(AR-MP)
2) Matching Pursuit(MP)
匹配追踪(MP)
1.
In this paper,the mutative scale chaos optimization algorithm is implemented to fast search for the approximately optimal atom at each step of Matching Pursuit(MP),and the speed of signal sparse decomposition is improved a lot.
本文利用变尺度混沌优化方法在优化搜索过程中不断缩小搜索空间,快速寻找匹配追踪(MP)过程中每一步的近似最佳原子,提高信号稀疏分解的速度,算法的有效性为实验结果所证实。
2.
The parameters set finally got by SA is regarded as that of the optimal atom at each step of Matching Pursuit(MP).
本文将模拟退火算法运用到信号的稀疏分解中,首先随机产生一组原子参数组,然后分别计算每个原子与信号或信号残差的内积的绝对值,找出内积绝对值最大的原子参数组并对它进行模拟退火处理,用处理的结果作为匹配追踪(MP)过程中每一步的最优解。
3) Cl-Ar correlation
Cl-Ar相关
4) match pursuit
匹配追踪
1.
SAR Super-Resolution Imaging Based on Match Pursuit Algorithm;
基于匹配追踪算法的SAR超分辨成像
2.
Research on the Method of SAR Images Super-resolution Based on Match Pursuit Algorithm;
基于匹配追踪算法的SAR图像提高分辨率方法研究
5) matching pursuit
匹配追踪
1.
Time-frequency filtering de-noise method based on matching pursuit algorithm;
基于匹配追踪算法的时频滤波去噪方法
2.
Spectral decomposition based on matching pursuit and its application
基于匹配追踪的谱分解方法及其应用
3.
In order to effectively abate the noise of geared pump vibration signal,matching pursuit is introduced for de-noising.
为了更有效地消除齿轮泵振动信号的噪声,引入匹配追踪消噪方法。
6) matching pursuit(MP)
匹配追踪
1.
After studying Matching Pursuit(MP) algorithm of signal sparse decomposition,this paper proposes a new approach to improve the speed of MP algorithm,and it describes how to build a Beowulf parallel computing system with 8 PCs.
在研究信号稀疏分解理论及其最常用的匹配追踪算法的基础上,针对MP算法存在的计算量过大的问题,提出一种基于并行计算系统实现信号稀疏分解的方法。
2.
Therefore,based on the method for the sparse decomposition,weak multicomponent LFM signals are decomposed on the over-complete dictionary of atoms by the matching pursuit(MP) algorithm.
文中以稀疏分解方法为基础,利用匹配追踪(MP)算法将微弱多分量LFM信号在过完备原子库上进行分解,由分解得到的原子参数可以估计出各个LFM信号的起始频率和调频斜率,从而实现了微弱多分量LFM信号的参数估计。
3.
Based on the over-complete multiscale dictionary of Spectrum atoms, the signal are decomposed into a linear expansion of atoms by the method of Matching Pursuit(MP), and FFT is applied to effectively reduce the time-complexity at each step of MP.
在过完备多尺度Spectrum原子库基础上,采用匹配追踪(MP)方法对信号进行原子分解,并通过FFT降低MP搜索过程的时间复杂性,在此基础上,对本征Spectrum原子参数进行有效降维,提取具有分类意义的原子特征向量,同步实现信号的自动分类和参数估计。
补充资料：自相关
自相关
auto - correlation
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说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条