Optimal acquisition policy for compressed measurements with limited observations

Sourabh Bhattacharya, Ashutosh Nayyar, Tamer Basar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we explore the problem of optimizing the measurement policy in finite horizon sequential compressive sensing when the number of samples are strictly restricted to be less than the overall horizon of the problem. We assume that at each instant the sensor can decide whether or not to take an observation, based on the quality of the sensing parameters. The objective of the sensor is to minimize the coherence of the final sensing matrix. This problem lies at the intersection of usage limited sensing [6], [11] and sequential compressive sensing [3]. First, we consider the optimal acquisition problem in the class of open-loop policies. We show that every open-loop policy that satisfies the sensing constraints is optimal. Next, we consider the set of closed-loop policies. In order to solve the optimal acquisition problem, we formulate the corresponding dynamic program. Finally, we propose a greedy strategy for acquiring measurements, and show that it is optimal for low-dimensional problems.

Original languageEnglish (US)
Title of host publicationConference Record of the 46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012
Pages968-972
Number of pages5
DOIs
StatePublished - Dec 1 2012
Event46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012 - Pacific Grove, CA, United States
Duration: Nov 4 2012Nov 7 2012

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012
CountryUnited States
CityPacific Grove, CA
Period11/4/1211/7/12

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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