Detection and tracking of multiple targets using wireless sensor networks - Detección y seguimiento de múltiples blancos en redes inalámbricas de sensores

This Ph.D. thesis is concerned with the development of algorithms for the detection and tracking of multiple targets using wireless sensor networks from the Bayesian standpoint. This is achieved by calculating the probability density function (PDF) of the multitarget state given the sensor measurements (posterior PDF) as it includes all the useful information to perform these tasks. The models of the target dynamics and the sensor measurements are usually nonlinear/non-Gaussian. Therefore, the posterior PDF cannot be calculated in closed form and approximations need to be made. Particle filters' approximations to the posterior PDF are convergent if the number of particles tends to infinity. However, in a practical situation, the computer power available is limited. As a result, the number of particles is bounded and particle filter performance is not guaranteed to be high. This decrease in performance due to the limited computational power is even more acute in a multiple target situation because of the high dimension of the state. Therefore, this thesis focuses on the development of particle filtering techniques with lower computational burden and higher performance than previously developed ones. Three different scenarios are considered: the detection and tracking of an unknown and variable number of targets using a sensor network, the tracking of targets when there is uncertainty in the sensor positions and the tracking of targets when a non-synchronised sensor network is used. As regards the detection and tracking of an unknown and variable number of targets, a particle filter with two layers is proposed to detect targets and an efficient algorithm, called the parallel partition method, is developed to track the detected targets. Also, a technique to extract target labelling information when there are two targets is proposed. That is, the filter is able to decide which target is which and determine the probability of error. The tracking of targets when there is uncertainty in the sensor positions is carried out by simultaneously localising the sensors and tracking the targets using simultaneous localisation and mapping (SLAM) techniques, traditionally used in the field of robotics. However, the multiple target nature of the problem implies that traditional SLAM techniques are not suitable and a new technique, which is based on the parallel partition method, is proposed to overcome the problems of conventional SLAM techniques. Additionally, the truncated Kalman filter also presented in this thesis is of great importance to estimate the positions of the sensors and is shown to be a very useful filtering technique that can be applied to a variety of filtering problems. When the sensors are not synchronised, conventional particle filtering techniques have a large computational load. Therefore, in this thesis, the asynchronous particle filter is proposed to lower their computational burden while providing accurate estimates

Two-layer particle filter for multiple target detection and tracking

This paper deals with the detection and tracking of an unknown number of targets using a Bayesian hierarchical model with target labels. To approximate the posterior probability density function, we develop a two-layer particle filter. One deals with track initiation, and the other with track maintenance. In addition, the parallel partition method is proposed to sample the states of the surviving targets

Particle filter for extracting target label information when targets move in close proximity

This paper addresses the problem of approximating the posterior probability density function of two targets after a crossing from the Bayesian perspective such that the information about target labels is not lost. To this end, we develop a particle filter that is able to maintain the inherent multimodality of the posterior after the targets have moved in close proximity. Having this approximation available, we are able to extract information about target labels even when the measurements do not provide information about target's identities. In addition, due to the structure of our particle filter, we are able to use an estimator that provides lower optimal subpattern assignment (OSPA) errors than usual estimators

Nonlinear filtering update phase via the Single Point Truncated Unscented Kalman filter

A fast algorithm to approximate the first two moments of the posterior probability density function (pdf) in nonlinear non-Gaussian Bayesian filtering is proposed. If the pdf of the measurement noise has a bounded support and the measurement function is continuous and bijective, we can use a modified prior pdf that meets Bayes' rule exactly. The central idea of this paper is that a Kalman filter applied to a modified prior distribution can improve the estimate given by the conventional Kalman filter. In practice, bounded support is not required and the modification of the prior is accounted for by adding an extra-point to the set of sigma-points used by the unscented Kalman filter