The problem of sparse signal recovery has received much attention recently with the development of compressed sensing. The underlying principles have general applicability and provide new and valuable tools to the practicing signal processing engineer. In this talk, we provide an overview of compressed sensing and the role of “sparsity” in signal processing along with the potential signal processing applications. We will then address the challenges underlying the sparse signal recovery problem, namely the problem of computing the sparsest solution to an undetermined linear system of equations. Unfortunately, the associated optimization problem is computationally complex (NP-hard) motivating the search for suboptimal algorithms, which offer a reasonable compromise between complexity and performance. In addition to discussing the computational algorithms, we will examine theoretical challenges such as the ability of the algorithms to identify the true sparse solution.
Bhaskar D. Rao received the B.Tech. degree in electronics and electrical communication engineering from the Indian Institute of Technology, Kharagpur, India, and the M.S. and Ph.D. degrees from the University of Southern California, Los Angeles, in 1981 and 1983, respectively. Since 1983, he has been with the University of California at San Diego, La Jolla, where he is currently a Professor with the Electrical and Computer Engineering Department. His interests are in the areas of digital signal processing, estimation theory, and optimization theory, with applications to digital communications, speech signal processing, and human-computer interactions.
He is the holder of the Ericsson endowed chair in Wireless Access Networks and was the Director of the Center for Wireless Communications (2008-2011). His research group has received several paper awards. Recently, a paper he co-authored with B. Song and R. Cruz received the 2008 Stephen O. Rice Prize Paper Award in the Field of Communications Systems and a paper he co-authored with S. Shivappa and M. Trivedi received the best paper award at AVSS 2008. He was elected to the fellow grade in 2000 for his contributions in high resolution spectral estimation.