Math @ Duke

Publications [#235972] of Robert Calderbank
Papers Published
 Calderbank, R; Howard, S; Jafarpour, S, Construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property,
IEEE Journal of Selected Topics in Signal Processing, vol. 4 no. 2
(2010),
pp. 358374, ISSN 19324553 [doi]
(last updated on 2017/12/14)
Abstract: Compressed Sensing aims to capture attributes of κsparse signals using very few measurements. In the standard compressed sensing paradigm, the N × C measurement matrix Φ is required to act as a near isometry on the set of all κsparse signals (restricted isometry property or RIP). Although it is known that certain probabilistic processes generate N × C matrices that satisfy RIP with high probability, there is no practical algorithm for verifying whether a given sensing matrix Φ has this property, crucial for the feasibility of the standard recovery algorithms. In contrast, this paper provides simple criteria that guarantee that a deterministic sensing matrix satisfying these criteria acts as a near isometry on an overwhelming majority of κsparse signals; in particular, most such signals have a unique representation in the measurement domain. Probability still plays a critical role, but it enters the signal model rather than the construction of the sensing matrix. An essential element in our construction is that we require the columns of the sensing matrix to form a group under pointwise multiplication. The construction allows recovery methods for which the expected performance is sublinear in C, and only quadratic in N, as compared to the superlinear complexity in C of the Basis Pursuit or Matching Pursuit algorithms; the focus on expected performance is more typical of mainstream signal processing than the worst case analysis that prevails in standard compressed sensing. Our framework encompasses many families of deterministic sensing matrices, including those formed from discrete chirps, DelsarteGoethals codes, and extended BCH codes. © IEEE.


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