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      Centralized and decentralized detection with cost-constrained measurements

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      Author(s)
      Laz, E.
      Gezici, Sinan
      Date
      2017
      Source Title
      Signal Processing
      Print ISSN
      0165-1684
      Publisher
      Elsevier B.V.
      Volume
      132
      Pages
      8 - 18
      Language
      English
      Type
      Article
      Item Usage Stats
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      Abstract
      Optimal detection performance of centralized and decentralized detection systems is investigated in the presence of cost constrained measurements. For the evaluation of detection performance, Bayesian, Neyman–Pearson and J-divergence criteria are considered. The main goal for the Bayesian criterion is to minimize the probability of error (more generally, the Bayes risk) under a constraint on the total cost of the measurement devices. In the Neyman–Pearson framework, the probability of detection is to be maximized under a given cost constraint. In the distance based criterion, the J-divergence between the distributions of the decision statistics under different hypotheses is maximized subject to a total cost constraint. The probability of error expressions are obtained for both centralized and decentralized detection systems, and the optimization problems are proposed for the Bayesian criterion. The probability of detection and probability of false alarm expressions are obtained for the Neyman–Pearson strategy and the optimization problems are presented. In addition, J-divergences for both centralized and decentralized detection systems are calculated and the corresponding optimization problems are formulated. The solutions of these problems indicate how to allocate the cost budget among the measurement devices in order to achieve the optimum performance. Numerical examples are presented to discuss the results.
      Keywords
      Centralized detection
      Decentralized detection
      Hypothesis testing
      Measurement cost
      Sensor networks
      Permalink
      http://hdl.handle.net/11693/37334
      Published Version (Please cite this version)
      http://dx.doi.org/10.1016/j.sigpro.2016.09.012
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