Felix Hermann has two new presentations out on the work performed at his lab at UBC:
There is also a new version of A fast algorithm for computing minimal-norm solutions to underdetermined systems of linear equations by Mark Tygert.
Finally, I just found this paper on arxiv: Compressive Sensing for MIMO Radar by Yao Yu, Athina Petropulu, H. Vincent Poor. The abstract reads:
Multiple-input multiple-output (MIMO) radar systems have been shown to achieve superior resolution as compared to traditional radar systems with the same number of transmit and receive antennas. This paper considers a distributed MIMO radar scenario, in which each transmit element is a node in a wireless network, and investigates the use of compressive sampling for direction-of-arrival (DOA) estimation. According to the theory of compressive sampling, a signal that is sparse in some domain can be recovered based on far fewer samples than required by the Nyquist sampling theorem. The DOA of targets form a sparse vector in the angle space, and therefore, compressive sampling can be applied for DOA estimation. The proposed approach achieves the superior resolution of MIMO radar with far fewer samples than other approaches. This is particularly useful in a distributed scenario, in which the results at each receive node need to be transmitted to a fusion center for further processing.
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