Browsing by Subject "CRLB"

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    Anchor placement in TOA based wireless localization networks via convex relaxation
    (IEEE, 2021-09-06) Öztürk, Cuneyd; Gezici, Sinan
    A wireless source localization network consisting of synchronized target and anchor nodes is considered. An anchor placement problem is formulated to minimize the Cramér-Rao lower bound (CRLB) on estimation of target node positions by anchor nodes. First, it is shown that the anchor placement problem can be approximated as a minimization problem of the ratio of two supermodular functions. Due to the lack of a polynomial time algorithm for such problems, an anchor selection problem is proposed to solve the anchor placement problem. Via relaxation of integer constraints, the anchor selection problem is approximated by a convex optimization problem, which is used to propose two algorithms for anchor selection. Furthermore, extensions to quasi-synchronous wireless localization networks are discussed. To examine the performance of the proposed algorithms, various simulation results are presented.
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    Cooperative positioning in wireless networks
    (John Wiley & Sons, 2016) Gholami, M. R.; Keskin, M. F.; Gezici, Sinan; Jansson, M.; Webster, J. G.
    In this article, we study cooperative positioning in wireless networks in which target nodes at unknown locations locally collaborate with each other to find their locations. We review different models available for positioning and categorize the model‐based algorithms in two groups: centralized and distributed. We then investigate a lower bound on the variance of unbiased estimators, namely the Cramer–Rao lower bound, which is a common benchmark in the positioning literature. We finally discuss some open problems and research topics in the area of positioning that are worth exploring in future studies.
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    Visible light positioning in the presence of malicious LED transmitters
    (IEEE, 2022-10-26) Kökdoğan, Furkan; Gezici, Sinan
    We consider a visible light positioning system in which a receiver performs position estimation based on signals emitted from a number of light emitting diode (LED) transmitters. Each LED transmitter can be malicious and transmit at an unknown power level with a certain probability. A maximum likelihood (ML) position estimator is derived based on the knowledge of probabilities that LED transmitters can be malicious. In addition, in the presence of training measurements, decision rules are designed for detection of malicious LED transmitters, and based on detection results, various ML based location estimators are proposed. To evaluate the performance of the proposed estimators, Cramér-Rao lower bounds (CRLBs) are derived for position estimation in scenarios with and without a training phase. Moreover, an ML estimator is derived when the probabilities that the LED transmitters can be malicious are unknown. The performances of all the proposed estimators are evaluated via numerical examples and compared against the CRLBs.