Browsing by Subject "Measurement cost"

Filter results by typing the first few letters
Now showing 1 - 5 of 5
  • Thumbnail Image
    ItemOpen Access
    Centralized and decentralized detection with cost-constrained measurements
    (2016-05) Laz, Eray
    In this thesis, 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 di erent 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.
  • Thumbnail Image
    ItemOpen Access
    Centralized and decentralized detection with cost-constrained measurements
    (Elsevier B.V., 2017) Laz, E.; Gezici, Sinan
    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.
  • Thumbnail Image
    ItemOpen Access
    A confidence ellipsoid approach for measurement cost minimization under Gaussian noise
    (IEEE, 2012-06) Dülek, Berkan; Gezici, Sinan
    The well-known problem of estimating an unknown deterministic parameter vector over a linear system subject to additive Gaussian noise is studied from the perspective of minimizing total sensor measurement cost under a constraint on the log volume of the estimation error confidence ellipsoid. A convex optimization problem is formulated for the general case, and a closed form solution is provided when the system matrix is invertible. Furthermore, effects of system matrix uncertainty are discussed by employing a specific but nevertheless practical uncertainty model. Numerical examples are presented to discuss the theoretical results in detail.
  • Thumbnail Image
    ItemOpen Access
    Cost minimization of measurement devices under estimation accuracy constraints in the presence of Gaussian noise
    (Elsevier, 2012) Dulek, B.; Gezici, Sinan
    Novel convex measurement cost minimization problems are proposed based on various estimation accuracy constraints for a linear system subject to additive Gaussian noise. Closed form solutions are obtained in the case of an invertible system matrix. In addition, the effects of system matrix uncertainty are studied both from a generic perspective and by employing a specific uncertainty model. The results are extended to the Bayesian estimation framework by treating the unknown parameters as Gaussian distributed random variables. Numerical examples are presented to discuss the theoretical results in detail.
  • Thumbnail Image
    ItemOpen Access
    Optimal cost allocation in centralized and decentralized detection problems
    (IEEE, 2016) Laz, Eray; Gezici, Sinan
    The optimal cost allocation problem is proposed for centralized and decentralized detection systems in the presence of cost constrained measurements, where the aim is to minimize the probability of error of a given detection system under a total cost constraint. The probability of error expressions are obtained for centralized and decentralized detection systems, and the optimal cost allocation strategies are provided. In addition, special cases are investigated in the presence of Gaussian observations and measurement noise. The solutions of the proposed problems specify the optimal allocation of the cost budget among various measurement devices (sensors) to achieve the optimum detection performance. Numerical examples are presented to discuss the implications of the results.