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

Interacting Multiple Model-Feedback Particle Filter for Stochastic Hybrid Systems

In this paper, a novel feedback control-based particle filter algorithm for the continuous-time stochastic hybrid system estimation problem is presented. This particle filter is referred to as the interacting multiple model-feedback particle filter (IMM-FPF), and is based on the recently developed feedback particle filter. The IMM-FPF is comprised of a series of parallel FPFs, one for each discrete mode, and an exact filter recursion for the mode association probability. The proposed IMM-FPF represents a generalization of the Kalmanfilter based IMM algorithm to the general nonlinear filtering problem. The remarkable conclusion of this paper is that the IMM-FPF algorithm retains the innovation error-based feedback structure even for the nonlinear problem. The interaction/merging process is also handled via a control-based approach. The theoretical results are illustrated with the aid of a numerical example problem for a maneuvering target tracking application

Bibliographic Review on Distributed Kalman Filtering

In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area

A track-before-detect labelled multi-Bernoulli particle filter with label switching

This paper presents a multitarget tracking particle filter (PF) for general track-before-detect measurement models. The PF is presented in the random finite set framework and uses a labelled multi-Bernoulli approximation. We also present a label switching improvement algorithm based on Markov chain Monte Carlo that is expected to increase filter performance if targets get in close proximity for a sufficiently long time. The PF is tested in two challenging numerical examples.Comment: Accepted for publication in IEEE Transactions on Aerospace and Electronic System

PPF - A Parallel Particle Filtering Library

We present the parallel particle filtering (PPF) software library, which enables hybrid shared-memory/distributed-memory parallelization of particle filtering (PF) algorithms combining the Message Passing Interface (MPI) with multithreading for multi-level parallelism. The library is implemented in Java and relies on OpenMPI's Java bindings for inter-process communication. It includes dynamic load balancing, multi-thread balancing, and several algorithmic improvements for PF, such as input-space domain decomposition. The PPF library hides the difficulties of efficient parallel programming of PF algorithms and provides application developers with the necessary tools for parallel implementation of PF methods. We demonstrate the capabilities of the PPF library using two distributed PF algorithms in two scenarios with different numbers of particles. The PPF library runs a 38 million particle problem, corresponding to more than 1.86 GB of particle data, on 192 cores with 67% parallel efficiency. To the best of our knowledge, the PPF library is the first open-source software that offers a parallel framework for PF applications.Comment: 8 pages, 8 figures; will appear in the proceedings of the IET Data Fusion & Target Tracking Conference 201

PHACT: parallel HOG and correlation tracking

Histogram of Oriented Gradients (HOG) based methods for the detection of humans have become one of the most reliable methods of detecting pedestrians with a single passive imaging camera. However, they are not 100 percent reliable. This paper presents an improved tracker for the monitoring of pedestrians within images. The Parallel HOG and Correlation Tracking (PHACT) algorithm utilises self learning to overcome the drifting problem. A detection algorithm that utilises HOG features runs in parallel to an adaptive and stateful correlator. The combination of both acting in a cascade provides a much more robust tracker than the two components separately could produce. © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only

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