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)
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 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 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)
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 equivalent to first-order Gauss causal Markov random processes. We establish that the DCT and the DST are exactly equivalent to(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)
We introduce in this paper a new fully distributed particle filter algorithm, referred to as the Random Exchange Diffusion Particle Filter (ReDif-PF), which is based on random information dissemination and, unlike previous consensus-based approaches, does not require iterative inter-node communication between measurement arrivals. The proposed algorithm is(More)
We introduce in this paper the fully distributed, Rao-Blackwellized Random Exchange Diffusion Particle Filter (RB ReDif-PF) to track a moving emitter using multiple received-signal-strength (RSS) sensors with unknown noise variances. In a simulated scenario with a partially connected network, the proposed RB ReDif-PF outperformed a suboptimal tracker that(More)