SCSToolboxV2
所属分类：matlab编程
开发工具：matlab
文件大小：11015KB
下载次数：71
上传日期：2012-06-29 10:10:42
上 传 者：
juanjuan10
说明： 将压缩感知用于谱估计中，根据论文谱压缩感知的一些程序
(Compressive sensing (CS) is a new approach to simultaneous sensing and compression of sparse
and compressible signals based on randomized dimensionality reduction. To recover a signal from its
compressive measurements, standard CS algorithms seek the sparsest signal in some discrete basis or
frame that agrees with the measurements. A great many applications feature smooth or modulated signals
that are frequency sparse and can be modeled as a superposition of a small number of sinusoids.
Unfortunately, such signals are only sparse in the discrete Fourier transform (DFT) domain when the
sinusoid frequencies live precisely at the center of the DFT bins. When this is not the case, CS recovery
performance degrades significantly. In this paper, we introduce a suite of spectral CS (SCS) recovery
algorithms for arbitrary frequency sparse signals. The key ingredients are an over-sampled DFT frame, a
signal model that inhibits closely spaced sinusoids, and classical sinusoid parameter e)
文件列表:
SCSToolboxV2 (0, 2010-09-20)
SCSToolboxV2\.DS_Store (6148, 2010-08-18)
__MACOSX (0, 2010-09-20)
__MACOSX\SCSToolboxV2 (0, 2010-09-20)
__MACOSX\SCSToolboxV2\._.DS_Store (82, 2010-08-18)
SCSToolboxV2\adjustfreqs.m (895, 2010-09-14)
SCSToolboxV2\axisfortex.m (122, 2010-09-14)
SCSToolboxV2\buildPhi_rdm.m (890, 2010-09-14)
SCSToolboxV2\buildSparseBasis.m (1739, 2010-09-14)
SCSToolboxV2\freqsignalest.m (780, 2010-09-14)
SCSToolboxV2\gp2_dsbfc_clean.mat (249049, 2010-09-14)
SCSToolboxV2\iht_fft.m (1225, 2010-09-14)
SCSToolboxV2\iht_periodogram.m (1543, 2010-09-14)
SCSToolboxV2\inhibition_ip.m (1366, 2010-09-14)
SCSToolboxV2\inhibition_reg_ip.m (1271, 2010-09-14)
SCSToolboxV2\modelapprox.m (2456, 2010-09-14)
SCSToolboxV2\modelapprox_ip.m (2028, 2010-09-14)
SCSToolboxV2\modelthresh.m (1678, 2010-09-14)
SCSToolboxV2\modelthresh_ip.m (1085, 2010-09-14)
SCSToolboxV2\musicapprox.m (994, 2010-09-14)
SCSToolboxV2\paper_amsignal_results.mat (3962, 2010-01-25)
SCSToolboxV2\paper_average_results.mat (44756, 2010-09-14)
SCSToolboxV2\paper_average_results_light.mat (37482, 2010-09-19)
SCSToolboxV2\paper_average_singletest_data.mat (2370932, 2010-09-14)
SCSToolboxV2\paper_average_singletest_results.mat (74515, 2010-09-14)
SCSToolboxV2\paper_average_singletest_results_light.mat (58625, 2010-09-19)
SCSToolboxV2\paper_best_results.mat (42713, 2010-09-14)
SCSToolboxV2\paper_best_results_light.mat (35372, 2010-09-19)
SCSToolboxV2\paper_gridres_results.mat (7582230, 2010-09-14)
SCSToolboxV2\paper_noise_results.mat (452856, 2010-09-14)
SCSToolboxV2\paper_resolution_results.mat (149776, 2010-09-14)
SCSToolboxV2\paper_sparseapprox_data.mat (14774, 2010-09-14)
SCSToolboxV2\paper_sparseapprox_results.mat (9384, 2010-09-14)
SCSToolboxV2\paper_sparseapprox_results_light.mat (8908, 2010-09-19)
SCSToolboxV2\paper_worst_results.mat (42623, 2010-09-14)
SCSToolboxV2\paper_worst_results_light.mat (35300, 2010-09-19)
SCSToolboxV2\paperfigures.m (8860, 2010-09-20)
__MACOSX\SCSToolboxV2\._paperfigures.m (171, 2010-09-20)
SCSToolboxV2\paperfigures_light.m (8147, 2010-09-20)
__MACOSX\SCSToolboxV2\._paperfigures_light.m (171, 2010-09-20)
... ...
README file for Spectral Compressive Sensing Toolbox V2.0
http://dsp.rice.edu/scs
September 20, 2010
By Marco F. Duarte
Program in Applied and Computational Mathematics
Princeton University
mduarte@princeton.edu
Note: The script rmAMSCS.m requires the l1-Magic toolbox, available at http://l1-magic.org
The toolbox contains the following files:
README.txt - This file.
CS and SCS Recovery algorithms
==============================
iht_fft.m - Iterative hard thresholding via Fast Fourier Transform
iht_periodogram.m - Iterative hard thresholding via DFT frame (periodogram)
siht_periodogram.m - Spectral iterative hard thresholding via periodogram (DFT frame)/heuristic
siht_periodogram_ip.m - Spectral iterative hard thresholding via periodogram (DFT frame)/integer program
siht_rootmusic.m - Spectral iterative hard thresholding via Root MUSIC (line spectral estimation)
Experiment scripts
==================
rmAMSCS.m - Test performance of CS and SCS on AM modulated signal measured with random demodulator.
test_scs_average.m - Tests performance of CS and SCS on frequency-sparse signals with arbitrary frequencies (Average case)
test_scs_best.m - Tests performance of CS and SCS on frequency-sparse signals without spectral leakage (Best case)
test_scs_gridres.m - Tests performance of SCS via Periodogram for a given frequency oversampling factor
test_scs_noise.m - Tests performance of SCS for varying measurement noise variances
test_scs_resolution.m - Tests performance of CS and SCS on frequency-sparse signals with components at neighboring frequencies.
test_scs_worst.m - Tests performance of CS and SCS on frequency-sparse signals with full spectral leakage (Worst case)
Figure-generating scripts for paper "Spectral Compressive Sensing"
==================================================================
paperfigures.m - Generates all figures in the paper. Data is generated by scripts listed below.
test_scs_amsignal_paper.m - Experiment for Figure 7
test_scs_average_paper.m - Experiment for Figure 3 (Average case)
test_scs_average_singletest.m - Experiment for Figure 1
test_scs_best_paper.m - Experiment for Figure 3 (Best case)
test_scs_gridres_paper.m - Experiment for figure 5
test_scs_noise_paper.m - Experiment for Figure 4
test_scs_resolution_paper.m - Experiment for Figure 6
test_scs_sparseapprox.m - Experiment for Figure 2
test_scs_worst_paper.m - Experiment for Figure 3 (Worst case)
Utilities
=========
adjustfreqs.m - Adjusts frequencies estimated from M signal samples to N signal samples
axisfortex.m - Labels the axis and title of a figure with a large font
buildPhi_rdm.m - Builds CS measurement matrix for random demodulator (Written by Jason Laska)
buildSparseBasis.m - Builds sparsity bases (Written by Jason Laska)
freqsignalest.m - Approximates a signal using frequency-sparse model
inhibition_ip.m - Formulates integer program to inhibit coherent frequencies
inhibition_reg_ip.m - Formulates integer program to inhibit coherent frequencies (regular frequency sampling)
modelapprox.m - Approximates a signal using coherence-inhibiting heuristic
modelapprox_ip.m - Approximates a signal using coherence-avoiding frequency-sparse model (integer program)
modelthresh.m - Approximates a frequency-sparse signal to K components using coherence-inhibiting heuristic
modelthresh.m - Approximates a frequency-sparse signal to K components using coherence-avoiding frequency-sparse model (integer program)
musicapprox.m - Approximates a signal using a K-component frequency-sparse model via Root MUSIC
rootmusic.m - Root MUSIC implementation (Written by Stephen Kogon)
Data inputs
==========
gp2_dsbfc_clean.mat - AM modulated signal data
paper_average_singletest_data.mat - Input signal data for Figure 1
paper_sparseapprox_data.mat - Input signal data for Figure 2
Data outputs
============
paper_amsignal_results.mat - Experimental results for Figure 7
paper_average_results.mat - Experimental results for Figure 3 (Average case)
paper_average_singletest_results.mat - Experimental results for Figure 1
paper_best_results.mat - Experimental results for Figure 3 (Best case)
paper_gridres_results.mat - Experimental results for Figure 5
paper_noise_results.mat - Experimental results for Figure 4
paper_resolution_results.mat - Experimental results for Figure 6
paper_sparseapprox_results.mat - Experimental results for Figure 2
paper_worst_results.mat - Experimental results for Figure 3 (Worst case)
Notes
=====
Many of the scripts mentioned above test a structured sparse approximation algorithm based on integer programming, which is computationally intensive. Alternative scripts are available with the suffix "_light" at the end of the filename.
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