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Here we describe novel Bayesian models for time-frequency analysis of nonstationary data. These models are based on the idea of a Gabor regression, in which a time series is represented as a superposition of time-frequency shifted versions of a simple window function whose essential support is well-localised in time and frequency. Specifically, we consider(More)
In this paper, we present an online approach for frequency tracking of a noisy sinusoid using sequential Monte Carlo (SMC) methods, also known as particle filters (PFs). In addition, apart from employing the classical Cartesian formulation model, we also develop two alternative dynamical models, namely, nearly constant frequency (NCF) and Singer, which are(More)
In this paper, we present an online stochastic approach for landmine detection based on ground penetrating radar (GPR) signals using sequential Monte Carlo (SMC) methods. Since the existence of true landmines is unknown and random, we propose to use the reversible jump Markov chain Monte Carlo (RJMCMC) in association with the SMC methods to jointly detect(More)
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