CS: CMP continued, Coherence, Compressive Sensing and Random Sensor Arrays

[There are 10 hours left  to solve this business model riddle]

Bob Sturm is continuing his investigation with the CMP reconstruction algorithm in:

• Recovery of Sparse Signals with Cyclic Matching Pursuit and Compressive Measurements: Now With Orthogonal Least Squares
• Recovery of Sparse Signals from Compressive Measurements: Now with Other Flavors

I also just found the following: Coherence, Compressive Sensing and Random Sensor Arrays by Lawrence Carin, Dehong Liu and Bin Guo. The abstract reads:

Random sensor arrays are examined from a compressive sensing (CS) perspective, particularly in terms of the coherence of CS matrices. It is demonstrated that the maximum sidelobe level of an array corresponds to the coherence of interest for CS. This understanding is employed to explicitly quantify the accuracy of array source localization, as a function of the number of sources and the noise level. The analysis demonstrates that the CS theory is applicable to arrays in vacuum as well as in the presence of a surrounding linear media; further, the presence of a surrounding media with known properties may be used to improve array performance, with this related to phase conjugation and time reversal. Several numerical results are presented to demonstrate the theory.

Image Credit: NASA/JPL/Space Science Institute, N00165424.jpg was taken on December 01, 2010 and received on Earth December 01, 2010. The camera was pointing toward SATURN at approximately 861,657 kilometers away, and the image was taken using the CL1 and CL2 filters.