Mónica F. Bugallo

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In this paper, we present a new method for stochastic simulation of coupled chemical reactions. In this method we obtain recursive expressions for propagating the first two moments of the probability distributions over time. Its advantage over other simulation methods is that it does not require Monte Carlo simulations, and hence it performs several orders(More)
We present particle filtering algorithms for tracking a single target using data from binary sensors. The sensors transmit signals that identify them to a central unit if the target is in their neighborhood; otherwise they do not transmit anything. The central unit uses a model for the target movement in the sensor field and estimates the target's(More)
Particle filtering is a sequential signal processing methodology that uses discrete random measures composed of particles and weights to approximate probability distributions of interest. The quality of approximation depends on many factors including the number of particles used for filtering and the way new particles are generated by the filter. The(More)
Accurate estimation of synchronization parameters is critical for reliable data detection in digital transmission. Although several techniques have been proposed in the literature for estimation of the reference parameters, i.e., timing, carrier phase, and carrier frequency offsets, they are based on either heuristic arguments or approximations, since(More)
In recent years, particle filtering has become a powerful tool for tracking signals and time-varying parameters of random dynamic systems. Thesemethods require amathematical representation of the dynamics of the system evolution, together with assumptions of probabilistic models. In this paper, we present a new class of particle filtering methods that do(More)
Recent advances of wireless sensor networks have presented some very interesting problems for signal processing. For practical reasons, many networks are composed of simple sensors that use very little power and do not consume much communication bandwidth. A class of sensors that satisfy these requirements are the tertiary sensors. They report an(More)
We propose algorithms for distributed sensor self-localization using beacon nodes. These beacon nodes broadcast some information which describes their positions. The sensor nodes with unknown location information utilize these descriptions along with the characteristics of received signals to obtain estimates of their positions. Sensors with resolved(More)
In recent years the theory of particle filtering has continued to advance, and it has found increasing use in sequential signal processing. A weakness of particle filtering is that it is inadequate for problems that besides tracking of evolving states require the estimation of constant parameters. In this paper, we propose particle filters that do not have(More)
Particle filtering methods aim at tracking probability distributions sequentially in time. One of the main challenges of these methods is their accuracy in high-dimensional state spaces. Namely, it can be shown that if the dimensions of these spaces are sufficiently high, the obtained results by particle filtering are practically useless. In this paper, we(More)
In this paper we consider the problem of target tracking in a network of mobile agents. We propose a scheme with agents that are endowed with processing and decision-making capabilities and without a central unit that controls them and/or fuses information. The agents measure received signal strengths from the targets and communicate it to the remaining(More)