Onur Ozdemir

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In this paper, we propose a new maximum-likelihood (ML) target localization approach which uses quantized sensor data as well as wireless channel statistics in a wireless sensor network. The novelty of our approach comes from the fact that statistics of imperfect wireless channels between sensors and the fusion center along with some physical layer design(More)
Wireless Sensor Networks (WSNs) are vulnerable to Byzantine attacks in which malicious sensors send falsified information to the Fusion Center (FC) with the goal of degrading inference performance. In this paper, we consider Byzantine attacks for the location estimation task in WSNs using binary quantized data. Posterior Cramér-Rao Lower Bound(More)
In this paper, a new framework for target tracking in a wireless sensor network using particle filters is proposed. Under this framework, the imperfect nature of the wireless communication channels between sensors and the fusion center along with some physical layer design parameters of the network are incorporated in the tracking algorithm based on(More)
The recursive procedure to compute the posterior Cramér-Rao lower bound (PCRLB) for sequential Bayesian estimators, derived by Tichavsky et al., provides an off-line performance bound for a general nonlinear filtering problem. Since the corresponding Fisher information matrix (FIM) is obtained by taking the expectation with respect to all the random(More)
In this article, we design a novel energy-detection based receiver architecture to detect UWB signals in a strong narrowband interference (NBI) environment. Designed receiver is capable of suppressing NBI at low cost without any need for searching its frequency location. This is made possible by preprocessing the received signal using a cascaded nonlinear(More)
We consider the problem of joint admission control and resource allocation in a secondary code-division network coexisting with a narrow-band primary system. Our objective is to find the maximum number of admitted secondary links and then find the optimal transmitting powers and code sequences of those secondary links such that the total energy consumption(More)
The performance of a modulation classifier is highly sensitive to channel signal-to-noise ratio (SNR). In this paper, we focus on amplitude-phase modulations and propose a modulation classification framework based on centralized data fusion using multiple radios and the hybrid maximum likelihood (ML) approach. In order to alleviate the computational(More)
We propose a modified Bayesian Cramér-Rao lower bound (BCRLB) for nonlinear tracking applications where the prediction distribution conditioned on past measurements is used as the prior. The novelty of the proposed modified BCRLB comes from the fact that it utilizes past measurements, therefore it is specific to the current realization of the track(More)
We consider the problem of joint admission control and power allocation in a multiband cognitive radio network (CRN) coexisting with multiple narrowband primary systems, and investigate two separate optimization problems: i) sum-rate maximization under primary user (PU) and secondary user (SU) quality of service (QoS) constraints; ii) sum-rate maximization(More)