Browsing by Subject "Sequential Monte Carlo methods"

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    Blind phase noise estimation in OFDM systems by sequential Monte Carlo method
    (Springer, 2006) Panayırcı, Erdal; Çırpan, H. A.; Moeneclaey, M.; Noels, N.
    In this paper, based on a sequential Monte Carlo method, a computationally efficient algorithm is presented for estimating the residual phase noise, blindly, generated at the output the phase tracking loop employed in OFDM systems. The basic idea is to treat the transmitted symbols as "missing data" and draw samples sequentially of them based on the observed signal samples up to time t. This way, the Bayesian estimates of the phase noise is obtained through these samples, sequentially drawn, together with their importance weights. The proposed receiver structure is seen to be ideally suited for high-speed parallel implementation using VLSI technology.
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    Superimposed event detection by sequential Monte Carlo methods
    (IEEE, 2007) Urfalıoğlu, O.; Kuruoğlu, E. E.; Çetin, A. Enis
    In this paper, we consider the detection of rare events by applying particle filtering. We model the rare event as an AR signal superposed on a background signal. The activation and deactivation times of the AR-signal are unknown. We solve the online detection problem of this superpositional rare event by extending the state space dimension by one. The additional parameter of the state represents the AR-signal, which is zero when deactivated. Numerical experiments demonstrate the effectiveness of our approach.