Marcelo G. S. Bruno

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In this paper, we introduce new algorithms for automatic tracking of multiaspect targets in cluttered image sequences. We depart from the conventional correlation filter/Kalman filter association approach to target tracking and propose instead a nonlinear Bayesian methodology that enables direct tracking from the image sequence incorporating the statistical(More)
We describe in this paper novel consensus-based distributed particle filtering algorithms which are applied to cooperative blind equalization of frequency-selective channels in a network with one transmitter and multiple receivers. The proposed algorithms employ parallel consensus averaging iterations to evaluate the product of some node-dependent(More)
We propose in this paper a new particle filtering algorithm for blind equalization of FIR frequency-selective communication channels corrupted by additive Gaussian noise, assuming that both the channel order and noise variance are unknown. The proposed algorithm integrates out analytically the unknown parameters using a modified sequential importance(More)
— We propose in this paper a mixed-state sequential Monte Carlo (SMC) filter for joint multiframe detection and tracking of multiaspect targets in cluttered image sequences. The proposed detector/tracker is a sampling/importance re-sampling (SIR) particle filter that uses resampling according to the weights to combat particle degeneracy and also includes an(More)
2571 [7] G. Carayannis et al., " A fast sequential algorithm for least-squares filtering and prediction, " IEEE Trans. Abstract— The correspondence addresses the intriguing question of which random models are equivalent to the discrete cosine transform (DCT) and discrete sine transform (DST). Common knowledge states that these transforms are asymptotically(More)
This paper introduces new algorithms for joint blind equalization and decoding of convolutionally coded communication systems operating on frequency-selective channels. The proposed method is based on particle filters (PF), recursively approximating maximum a posteriori (MAP) estimates of the transmitted data without explicitly determining channel(More)
This paper introduces new cooperative particle filter algorithms for tracking emitters using received-signal strength (RSS) measurements. In the studied scenario, multiple RSS sensors passively observe different attenuated and noisy versions of the same signal originating from a moving emitter and cooperate to estimate the emitter state. Assuming unknown(More)
We introduce in this paper a novel cooperative particle filter algorithm for tracking a moving emitter using received-signal strength (RSS) measurements with unknown observation noise variance. In the studied scenario, multiple RSS sensors passively observe independently attenuated and perturbed versions of the same broadcast signal transmitted by an(More)