Browsing by Subject "Cramer Rao lower bound"
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Item Open Access A Bayesian approach to respiration rate estimation via pulse-based ultra-wideband signals(IEEE, 2009) Soǧancı, Hamza; Gezici, Sinan; Arıkan, OrhanIn this paper, theoretical limits on estimation of respiration rates via pulse-based ultra-wideband (UWB) signals are studied in the presence of prior information about respiration related signal parameters. First, a generalized Cramer-Rao lower bound (G-CRLB) expression is derived, and then simplified versions of the bound are obtained for sinusoidal displacement functions. In addition to the derivation of the theoretical limits, a two-step suboptimal estimator based on matched filter (correlation) processing and maximum a posteriori probability (MAP) estimation is proposed. It is shown that the proposed estimator performs very closely to the theoretical limits under certain conditions. Simulation results are presented to investigate the theoretical results.Item Open Access Hybrid TW-TOA/TDOA positioning algorithms for cooperative wireless networks(IEEE, 2011) Gholami, M.R.; Gezici, Sinan; Ström, E.G.; Rydström, M.The problem of positioning an unknown target is studied for a cooperative wireless sensor network using hybrid two-way time-of-arrival and time-difference-of-arrival measurements. A maximum likelihood estimator (MLE) can be employed to solve the problem. Due to the non-linear nature of the cost function in the MLE, a numerical method, e.g., an iterative search algorithm with a good initial point, should be taken to accurately estimate the target. To avoid drawbacks in a numerical method, we instead linearize the measurements and obtain a new two-step estimator that has a closed-form solution in each step. Simulation results confirm that the proposed linear estimator can attain Cramer-Rao lower bound for sufficiently high SNR. © 2011 IEEE.Item Open Access Time delay estimation in cognitive radio systems(IEEE, 2009-12) Koçak, Fatih; Çelebi, H.; Gezici, Sinan; Qaraqe, K. A.; Arslan, H.; Poor, H. V.In cognitive radio systems, secondary users can utilize multiple dispersed bands that are not used by primary users. In this paper, time delay estimation of signals that occupy multiple dispersed bands is studied. First, theoretical limits on time delay estimation are reviewed. Then, two-step time delay estimators that provide trade-offs between computational complexity and performance are investigated. In addition, asymptotic optimality properties of the two-step time delay estimators are discussed. Finally, simulation results are presented to explain the theoretical results. © 2009 IEEE.Item Open Access Time-delay estimation in dispersed spectrum cognitive radio systems(SpringerOpen, 2010) Kocak, F.; Celebi, H.; Gezici, Sinan; Qaraqe, K. A.; Arslan, H.; Poor, H. V.Time-delay estimation is studied for cognitive radio systems, which facilitate opportunistic use of spectral resources. A two-step approach is proposed to obtain accurate time-delay estimates of signals that occupy multiple dispersed bands simultaneously, with significantly lower computational complexity than the optimal maximum likelihood (ML) estimator. In the first step of the proposed approach, an ML estimator is used for each band of the signal in order to estimate the unknown parameters of the signal occupying that band. Then, in the second step, the estimates from the first step are combined in various ways in order to obtain the final time-delay estimate. The combining techniques that are used in the second step are called optimal combining, signal-to-noise ratio (SNR) combining, selection combining, and equal combining. It is shown that the performance of the optimal combining technique gets very close to the Cramer-Rao lower bound at high SNRs. These combining techniques provide various mechanisms for diversity combining for time-delay estimation and extend the concept of diversity in communications systems to the time-delay estimation problem in cognitive radio systems. Simulation results are presented to evaluate the performance of the proposed estimators and to verify the theoretical analysis.