Mónica F. Bugallo

Target Tracking by Particle Filtering in Binary Sensor Networks

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 targets trajectory, velocity, and power using the received data. We propose and implement the tracking by employing auxiliary particle filtering and cost-reference particle filtering. Unlike auxiliary particle filtering, cost-reference particle filtering does not rely on any probabilistic assumptions about the dynamic system. In the paper, we also extend the method to include estimation of constant parameters, and we derive the posterior Cramer-Rao bounds (PCRBs) for the states. We show the performances of the proposed methods by extensive computer simulations and compare them to the derived bounds.

A new class of particle filters for random dynamic systems with unknown statistics

In recent years, particle filtering has become a powerful tool for tracking signals and time-varying parameters of random dynamic systems. These methods require a mathematical 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 not assume explicit mathematical forms of the probability distributions of the noise in the system. As a consequence, the proposed techniques are simpler, more robust, and more flexible than standard particle filters. Apart from the theoretical development of specific methods in the new class, we provide computer simulation results that demonstrate the performance of the algorithms in the problem of autonomous positioning of a vehicle in a-dimensional space.

Multiple Particle Filtering

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 problem of good approximation becomes increasingly challenging as the dimension of the state space increases. In this paper, we address a possible solution for improved particle filtering in high dimensional cases by using a set of particle filters operating on partitioned subspaces of the complete state space. We provide simulation results that show the feasibility of the proposed approach.

Signal processing by particle filtering for binary sensor networks

In a wireless sensor network, limited power, communication, and computational resources are the major constraints that have to be overcome for their successful deployment and utilization. Binary sensor networks are a class of networks that get around these constraints. There, the sensors transmit only a binary digit on the occurrence of the event of interest, and therefore, the signals that reach the fusion center of these networks are highly compressed and pose challenging problems for recovering the sensed information. We consider the problem of tracking a vehicle, which moves along a 2-dimensional space, by using a binary sensor network that fuses information by particle filtering.

Target Tracking by Multiple Particle Filtering

In this paper we address the problem of tracking of multiple targets in a wireless sensor network using particle filtering. This methodology approximates the probability distributions of the objects of interest by using random measures composed of particles and associated weights. An important challenge of the resulting algorithms is the need for very large number of particles when the dimensions of the states are even moderately large. We propose to combat this problem by alternative particle filtering implementations where we partition the state space of the system into different subspaces and run a separate particle filter for each subspace. The performance of the considered algorithm is illustrated through computer simulations that show considerable advantage of the proposed method over the standard particle filter.

Performance comparison of EKF and particle filtering methods for maneuvering targets

Online tracking of maneuvering targets is a highly nonlinear and challenging problem that involves, at every time instant, the estimation not only of the unknown state in the dynamic model describing the evolution of the target, but also the underlying model accounting for the regime of movement. In this paper we review and compare several sequential estimation procedures, that use appropriate strategies for coping with various models that account for the different modes of operation. We focus on the application of the recently proposed cost-reference particle filtering (CRPF) methodology, which aims at the estimation of the system state without using probability distributions. The resulting method has a more robust performance when compared to standard particle filtering (SPF) algorithms or the interactive multiple model (IMM) algorithm based on the use of the well known extended Kalman filter (EKF). Advantages and disadvantages of the considered algorithms are illustrated and discussed through computer simulations.

Sensor self-localization with beacon position uncertainty

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 positions, in the successive stages of the algorithm also broadcast their location information to other sensors so that they can resolve their own positions. Conditional upon the availability of probabilistic distributions of noise processes, we propose iterative and Monte Carlo sampling-based methods for obtaining sensor location descriptions. We also provide approximate hybrid Cramer-Rao bounds for distributed sensor self-localization and compare them with the proposed algorithms. We demonstrate the performance of the proposed algorithms through extensive computer simulations.

A sequential Monte Carlo method for adaptive blind timing estimation and data detection

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 optimal estimation is analytically intractable in most practical setups. In this paper, we introduce a new alternative approach for blind synchronization and data detection derived within the Bayesian framework and implemented via the sequential Monte Carlo (SMC) methodology. By considering an extended dynamic system where the reference parameters and the transmitted symbols are system-state variables, the proposed SMC technique guarantees asymptotically minimal symbol error rate when it is combined with adequate receiver architectures, both in open-loop and closed-loop configurations. The performance of the proposed technique is studied analytically, by deriving the posterior Cra/spl acute/mer-Rao bound for timing estimation and through computer simulations that illustrate the overall performance of the resulting receivers.

Tracking with particle filtering in tertiary wireless sensor networks

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 approaching event with one signal and a receding event with another signal. When the event is out of their range, they do not report anything. In this paper, we apply particle filtering for processing signals from tertiary sensor networks with the purpose of tracking events (targets) within the field of the sensor network. We present an algorithm for tracking and demonstrate its performance by computer simulations.

Density assisted particle filters for state and parameter estimation

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 this limitation. We call these filters density assisted particle filters, of which special cases are the recently introduced Gaussian particle filters and Gaussian sum particle filters. An implementation of a density particle filter is shown on a relatively simple but important nonlinear model. Simulations are included that show the performance of this filter.