81 research outputs found
Performance of Sampling/Resampling-based Particle Filters Applied to Non-Linear Problems
In this work, we propose a wireless body area sensor network (WBASN) to monitor patient position. Localization and tracking are enhanced by improving the effect of the received signal strength (RSS) variation. First, we propose a modified particle filter (PF) that adjusts resampling parameters for the Kullback-Leibler distance (KLD)-resampling algorithm to ameliorate the effect of RSS variation by generating a sample set near the high-likelihood region. The key issue of this method is to use a resampling parameter lower bound for reducing both the root mean square error (RMSE) and the mean number of particles used. To determine this lower bound, an optimal algorithm is proposed based on the maximum RMSE between the proposed algorithm and the KLD-resampling algorithm or based on the maximum mean number of particles used of these algorithms. Finally, PFs based on KLD-sampling and KLD-resampling are proposed to minimize the efficient number of particles and to reduce the estimation error compared to traditional algorithms
Data Analysis and Memory Methods for RSS Bluetooth Low Energy Indoor Positioning
The thesis aims at finding a feasible solution to Bluetooth low energy indoor positioning (BLE-IP) including comprehensive data analysis of the received signal strength indication (RSSI) values. The data analysis of RSSI values was done to understand different factors influencing the RSSI values so as to gain better understanding of data generating process and to improve the data model. The positioning task is accomplished using a methodology called \textit{fingerprinting}. The fingerprinting based positioning involves two phases namely \textit{calibration phase} and \textit{localization phase}. The localization phase utilises the memory methods for positioning. In this thesis, we have used \textit{Gaussian process} for generation of radio maps and for localization we focus on memory methods: \textit{particle filters} and \textit{unscented Kalman filters}. The Gaussian process radio map is used as the measurement model in the Bayesian filtering context. The optimal fingerprinting phase parameters were determined and the filtering methods were evaluated in terms root mean square error
Particle filters for tracking in wireless sensor networks
The goal of this thesis is the development, implementation and
assessment of efficient particle filters (PFs) for various target tracking
applications on wireless sensor networks (WSNs).
We first focus on developing efficient models and particle filters for
indoor tracking using received signal strength (RSS) in WSNs. RSS is
a very appealing type of measurement for indoor tracking because of its
availability on many existing communication networks. In particular, most
current wireless communication networks (WiFi, ZigBee or even cellular
networks) provide radio signal strength (RSS) measurements for each radio
transmission. Unfortunately, RSS in indoor scenarios is highly influenced
by multipath propagation and, thus, it turns out very hard to adequately
model the correspondence between the received power and the transmitterto-
receiver distance. Further, the trajectories that the targets perform in
indoor scenarios usually have abrupt changes that result from avoiding walls
and furniture and consequently the target dynamics is also difficult to model.
In Chapter 3 we propose a flexible probabilistic scheme that allows
the description of different classes of target dynamics and propagation
environments through the use of multiple switching models. The resulting
state-space structure is termed a generalized switching multiple model
(GSMM) system. The drawback of the GSMM system is the increase in the
dimension of the system state and, hence, the number of variables that the
tracking algorithm has to estimate. In order to handle the added difficulty,
we propose two Rao-Blackwellized particle filtering (RBPF) algorithms in
which a subset of the state variables is integrated out to improve the tracking
accuracy.
As the main drawback of the particle filters is their computational
complexity we then move on to investigate how to reduce it via de
distribution of the processing. Distributed applications of tracking are
particularly interesting in situations where high-power centralized hardware
cannot be used. For example, in deployments where computational infrastructure and power are not available or where there is no time or
trivial way of connecting to it. The large majority of existing contributions
related to particle filtering, however, only offer a theoretical perspective or
computer simulation studies, owing in part to the complications of real-world
deployment and testing on low-power hardware.
In Chapter 4 we investigate the use of the distributed resampling with non-proportional allocation (DRNA) algorithm in order to obtain
a distributed particle filtering (DPF) algorithm. The DRNA algorithm
was devised to speed up the computations in particle filtering via the
parallelization of the resampling step. The basic assumption is the
availability of a set of processors interconnected by a high-speed network,
in the manner of state-of-the-art graphical processing unit (GPU) based
systems. In a typical WSN, the communications among nodes are subject
to various constraints (i.e., transmission capacity, power consumption or
error rates), hence the hardware setup is fundamentally different.
We first revisit the standard PF and its combination with the DRNA
algorithm, providing a formal description of the methodology. This includes
a simple analysis showing that (a) the importance weights are proper and
(b) the resampling scheme is unbiased. Then we address the practical
implementation of a distributed PF for target tracking, based on the DRNA
scheme, that runs in real time over a WSN. For the practical implementation
of the methodology on a real-time WSN, we have developed a software
and hardware testbed with the required algorithmic and communication
modules, working on a network of wireless light-intensity sensors.
The DPF scheme based on the DRNA algorithm guarantees the
computation of proper weights and consistent estimators provided that the
whole set of observations is available at every time instant at every node.
Unfortunately, due to practical communication constraints, the technique
described in Chapter 4 may turn out unrealistic for many WSNs of larger
size. We thus investigate in Chapter 5 how to relax the communication
requirements of the DPF algorithm using (a) a random model for the spread
of data over the WSN and (b) methods that enable the out-of-sequence
processing of sensor observations. The presented observation spread scheme
is flexible and allows tuning of the observation spread over the network
via the selection of a parameter. As the observation spread has a direct
connection with the precision on the estimation, we have also introduced a methodology that allows the selection of the parameter a priori without
the need of performing any kind of experiment. The performance of the
proposed scheme is assessed by way of an extensive simulation study.De forma general, el objetivo de esta tesis doctoral es el desarrollo y la
aplicación de filtros de partículas (FP) eficientes para diversas aplicaciones
de seguimiento de blancos en redes de sensores inalámbricas (wireless sensor
networks o WSNs).
Primero nos centramos en el desarrollo de modelos y filtros de partículas
para el seguimiento de blancos en entornos de interiores mediante el uso de
medidas de potencia de señal de radio (received signal strength o RSS) en
WSNs. Las medidas RSS son un tipo de medida muy utilizada debido a
su disponibilidad en redes ya implantadas en muchos entornos de interiores.
De hecho, en muchas redes de comunicaciones inalámbricas actuales (WiFi,
ZigBee o incluso las redes de telefonía móvil), se pueden obtener medidas
de RSS sin necesidad de modificación alguna. Desafortunadamente,
las medidas RSS en entornos de interiores suelen distorsionarse debido
a la propagación multitrayecto por lo que resulta muy difícil modelar
adecuadamente la relación entre la potencia de señal recibida y la distancia
entre el transmisor y el receptor. Otra dificultad añadida en el seguimiento
de interiores es que las trayectorias realizadas por los blancos suelen tener
por lo general cambios muy bruscos y en consecuencia el modelado de las
trayectorias dinámicas es una actividad muy compleja.
En el Capítulo 3 se propone un esquema probabilístico flexible que
permite la descripción de los diferentes sistemas dinámicos y entornos
de propagación mediante el uso de múltiples modelos conmutables entre
sí. Este esquema permite la descripción de varios modelos dinámicos y
de propagación de forma muy precisa de manera que el filtro sólo tiene
que estimar el modelo adecuado a cada instante para poder hacer el
seguimiento. El modelo de estado resultante (modelo de conmutación múltiple generalizado, generalized switiching multiple model o GSMM) tiene
el inconveniente del aumento de la dimensión del estado del sistema y, por
lo tanto, el número de variables que el algoritmo de seguimiento tiene que
estimar. Para superar esta dificultad, se proponen varios algoritmos de filtros de partículas con reducción de la varianza (Rao-Blackwellized particle
filtering (RBPF) algorithms) en el que un subconjunto de las variables de
estado, incluyendo las variables indicadoras de observación, se integran a fin
de mejorar la precisión de seguimiento.
Dado que la mayor desventaja de los filtros de partículas es su
complejidad computacional, a continuación investigamos cómo reducirla
distribuyendo el procesado entre los diferentes nodos de la red. Las aplicaciones distribuidas de seguimiento en redes de sensores son de especial
interés en muchas implementaciones reales, por ejemplo: cuando el hardware
usado no tiene suficiente capacidad computacional, si se quiere alargar la
vida de la red usando menos energía, o cuando no hay tiempo (o medios)
para conectarse a la toda la red. La reducción de complejidad también es
interesante cuando la red es tan extensa que el uso de hardware con alta
capacidad de procesamiento la haría excesivamente costosa.
La mayoría de las contribuciones existentes ofrecen exclusivamente una
perspectiva teórica o muestran resultados sintéticos o simulados, debido en
parte a las complicaciones asociadas a la implementación de los algoritmos y
de las pruebas en un hardware con nodos de baja capacidad computacional.
En el Capítulo 4 se investiga el uso del algoritmo distributed resampling
with non proportional allocation (DRNA) a fin de obtener un filtro de
partículas distribuido (FPD) para su implementación en una red de sensores
real con nodos de baja capacidad computacional. El algoritmo DRNA fue
elaborado para acelerar el cómputo del filtro de partículas centrándose en la
paralelización de uno de sus pasos: el remuestreo. Para ello el DRNA asume
la disponibilidad de un conjunto de procesadores interconectados por una
red de alta velocidad.
En una red de sensores inalábmrica, las comunicaciones entre los nodos
suelen tener restricciones (debido a la capacidad de transmisión, el consumo
de energía o de las tasas de error), y en consecuencia, la configuración de
hardware es fundamentalmente diferente. En este trabajo abordamos el
problema de la aplicación del algoritmo de DRNA en una WSN real. En
primer lugar, revisamos el FP estándar y su combinación con el algoritmo
DRNA, proporcionando una descripci´on formal de la metodología. Esto
incluye un análisis que demuestra que (a) los pesos se calculan de forma
adecuada y (b) que el paso del remuestreo no introduce ningún sesgo. A
continuación describimos la aplicación práctica de un FP distribuido para
seguimiento de objetivos, basado en el esquema DRNA, que se ejecuta en tiempo real a través de una WSN. Hemos desarrollado un banco de
pruebas de software y hardware donde hemos usado unos nodos con sensores
que miden intensidad de la luz y que a su vez tienen una capacidad de
procesamiento y de comunicaciones limitada. Evaluamos el rendimiento
del sistema de seguimiento en términos de error de la trayectoria estimada
usando los datos sintéticos y evaluamos la capacidad computacional con
datos reales.
El filtro de partículas distribuído basado en el algoritmo DRNA garantiza
el cómputo correcto de los pesos y los estimadores a condición de que
el conjunto completo de observaciones estén disponibles en cada instante de tiempo y en cada nodo. Debido a limitaciones de comunicación esta
metodología puede resultar poco realista para su implementación en muchas
redes de sensores inalámbricas de tamaño grande. Por ello, en el Capítulo
5 investigamos cómo reducir los requisitos de comunicación del algoritmo
anterior mediante (a) el uso de un modelo aleatorio para la difusión de
datos de observación a través de las red y (b) la adaptación de los filtros para
permitir el procesamiento de observaciones que lleguen fuera de secuencia.
El esquema presentado permite reducir la carga de comunicaciones en la
red a cambio de una reducción en la precisión del algoritmo mediante la
selección de un parámetro de diseño. También presentamos una metodología
que permite la selección de dicho parámetro que controla la difusión de
las observaciones a priori sin la necesidad de llevar a cabo ningún tipo
de experimento. El rendimiento del esquema propuesto ha sido evaluado
mediante un estudio extensivo de simulaciones
Improvement Schemes for Indoor Mobile Location Estimation: A Survey
Location estimation is significant in mobile and ubiquitous computing systems. The complexity and smaller scale of the indoor environment impose a great impact on location estimation. The key of location estimation lies in the representation and fusion of uncertain information from multiple sources. The improvement of location estimation is a complicated and comprehensive issue. A lot of research has been done to address this issue. However, existing research typically focuses on certain aspects of the problem and specific methods. This paper reviews mainstream schemes on improving indoor location estimation from multiple levels and perspectives by combining existing works and our own working experiences. Initially, we analyze the error sources of common indoor localization techniques and provide a multilayered conceptual framework of improvement schemes for location estimation. This is followed by a discussion of probabilistic methods for location estimation, including Bayes filters, Kalman filters, extended Kalman filters, sigma-point Kalman filters, particle filters, and hidden Markov models. Then, we investigate the hybrid localization methods, including multimodal fingerprinting, triangulation fusing multiple measurements, combination of wireless positioning with pedestrian dead reckoning (PDR), and cooperative localization. Next, we focus on the location determination approaches that fuse spatial contexts, namely, map matching, landmark fusion, and spatial model-aided methods. Finally, we present the directions for future research
An Introduction to Twisted Particle Filters and Parameter Estimation in Non-linear State-space Models
Twisted particle filters are a class of sequential Monte Carlo methods
recently introduced by Whiteley and Lee to improve the efficiency of marginal
likelihood estimation in state-space models. The purpose of this article is to
extend the twisted particle filtering methodology, establish accessible
theoretical results which convey its rationale, and provide a demonstration of
its practical performance within particle Markov chain Monte Carlo for
estimating static model parameters. We derive twisted particle filters that
incorporate systematic or multinomial resampling and information from
historical particle states, and a transparent proof which identifies the
optimal algorithm for marginal likelihood estimation. We demonstrate how to
approximate the optimal algorithm for nonlinear state-space models with
Gaussian noise and we apply such approximations to two examples: a range and
bearing tracking problem and an indoor positioning problem with Bluetooth
signal strength measurements. We demonstrate improvements over standard
algorithms in terms of variance of marginal likelihood estimates and Markov
chain autocorrelation for given CPU time, and improved tracking performance
using estimated parameters.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
Personal Navigation Based on Wireless Networks and Inertial Sensors
Tato práce se zaměřuje na vývoj navigačního algoritmu pro systémy vhodné k lokalizaci osob v budovách a městských prostorech. Vzhledem k požadovaným nízkým nákladům na výsledný navigační systém byla uvažována integrace levných inerciálních senzorů a určování vzdálenosti na základě měření v bezdrátových sítích. Dále bylo předpokládáno, že bezdrátová síť bude určena k jiným účelům (např: měření a regulace), než lokalizace, proto bylo použito měření síly bezdrátového signálu. Kvůli snížení značné nepřesnosti této metody, byla navrhnuta technika mapování ztrát v bezdrátovém kanálu. Nejprve jsou shrnuty různé modely senzorů a prostředí a ty nejvhodnější jsou poté vybrány. Jejich efektivní a nové využití v navigační úloze a vhodná fůze všech dostupných informací jsou hlavní cíle této práce.This thesis deals with navigation system based on wireless networks and inertial sensors. The work aims at a development of positioning algorithm suitable for low-cost indoor or urban pedestrian navigation application. The sensor fusion was applied to increase the localization accuracy. Due to required low application cost only low grade inertial sensors and wireless network based ranging were taken into account. The wireless network was assumed to be preinstalled due to other required functionality (for example: building control) therefore only received signal strength (RSS) range measurement technique was considered. Wireless channel loss mapping method was proposed to overcome the natural uncertainties and restrictions in the RSS range measurements. The available sensor and environment models are summarized first and the most appropriate ones are selected secondly. Their effective and novel application in the navigation task, and favorable fusion (Particle filtering) of all available information are the main objectives of this thesis.
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Wireless indoor localisation within the 5G internet of radio light
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonNumerous applications can be enhanced by accurate and efficient indoor localisation using wireless
sensor networks, however trade-offs often exist between these two parameters. In this thesis, realworld
and simulation data is used to examine the hybrid millimeter wave and Visible Light
Communications (VLC) architecture of the 5G Internet of Radio Light (IoRL) Horizon 2020 project.
Consequently, relevant localisation challenges within Visible Light Positioning (VLP) and asynchronous
sampling networks are identified, and more accurate and efficient solutions are developed.
Currently, VLP relies strongly on the assumed Lambertian properties of light sources.
However, in practice, not all lights are Lambertian. To support the widespread deployment of VLC
technology in numerous environments, measurements from non-Lambertian sources are analysed to
provide new insights into the limitations of existing VLP techniques. Subsequently, a novel VLP
calibration technique is proposed, and results indicate a 59% accuracy improvement against existing
methods. This solution enables high accuracy centimetre level VLP to be achieved with non-
Lambertian sources.
Asynchronous sampling of range-based measurements is known to impact localisation
performance negatively. Various Asynchronous Sampling Localisation Techniques (ASLT) exist to
mitigate these effects. While effective at improving positioning performance, the exact suitability of
such solutions is not evident due to their additional processes, subsequent complexity, and increased
costs. As such, extensive simulations are conducted to study the effectiveness of ASLT under variable
sampling latencies, sensor measurement noise, and target trajectories. Findings highlight the
computational demand of existing ASLT and motivate the development of a novel solution. The
proposed Kalman Extrapolated Least Squares (KELS) method achieves optimal localisation
performance with a significant energy reduction of over 50% when compared to current leading ASLT.
The work in this thesis demonstrates both the capability for high performance VLP from non-
Lambertian sources as well as the potential for energy efficient localisation for sequentially sampled
range measurements.Horizon 202
Sensor Fusion for Mobile Robot Localization using UWB and ArUco Markers
Uma das principais características para considerar um robô autónomo é o facto de este ser capaz de se localizar, em tempo real, no seu ambiente, ou seja saber a sua posição e orientação. Esta é uma área desafiante que tem sido estudada por diversos investigadores em todo o mundo. Para obter a localização de um robô é possível recorrer a diferentes metodologias. No entanto há metodologias que apresentam problemas em diferentes circunstâncias, como é o caso da odometria que sofre de acumulação de erros com a distância percorrida pelo robô. Outro problema existente em diversas metodologias é a incerteza na deteção do robô devido a ruído presente nos sensores. Com o intuito de obter uma localização mais robusta do robô e mais tolerante a falhas é possível combinar diversos sistemas de localização, combinando assim as vantagens de cada um deles.
Neste trabalho, será utilizado o sistema Pozyx, uma solução de baixo custo que fornece informação de posicionamento com o auxílio da tecnologia Ultra-WideBand Time-of-Flight (UWB ToF). Também serão utilizados marcadores ArUco colocados no ambiente que através da sua identificação por uma câmara é também possível obter informação de posicionamento. Estas duas soluções irão ser estudadas e implementadas num robô móvel, através de um esquema de localização baseada em marcadores. Primeiramente, irá ser feita uma caracterização do erro de ambos os sistemas, uma vez que as medidas não são perfeitas, havendo sempre algum ruído nas medições. De seguida, as medidas fornecidas pelos sistemas irão ser filtradas e fundidas com os valores da odometria do robô através da implementação de um Filtro de Kalman Extendido (EKF). Assim, é possível obter a pose do robô (posição e orientação), pose esta que é comparada com a pose fornecida por um sistema de Ground-Truth igualmente desenvolvido para este trabalho com o auxílio da libraria ArUco, percebendo assim a precisão do algoritmo desenvolvido.
O trabalho desenvolvido mostrou que com a utilização do sistema Pozyx e dos marcadores ArUco é possível melhorar a localização do robô, o que significa que é uma solução adequada e eficaz para este fim.One of the main characteristics to consider a robot truly autonomous is the fact that it is able to locate itself, in real time, in its environment, that is, to know its position and orientation. This is a challenging area that has been studied by several researchers around the world. To obtain the localization of a robot it is possible to use different methodologies. However, there are methodologies that present problems in different circumstances, as is the case of odometry that suffers from error accumulation with the distance traveled by the robot. Another problem existing in several methodologies is the uncertainty in the sensing of the robot due to noise present in the sensors. In order to obtain a more robust localization of the robot and more fault tolerant it is possible to combine several localization systems, thus combining the advantages of each one.
In this work, the Pozyx system will be used, a low-cost solution that provides positioning information through Ultra-WideBand Time-of-Flight (UWB ToF) technology. It will also be used ArUco markers placed in the environment that through their identification by a camera it is also possible to obtain positioning information. These two solutions will be studied and implemented in a mobile robot, through a beacon-based localization scheme. First, an error characterization of both systems will be performed, since the measurements are not perfect, and there is always some noise in the measurements. Next, the measurements provided by the systems will be filtered and fused with the robot's odometry values by the implementation of an Extended Kalman Filter (EKF). In this way, it is possible to obtain the robot's pose, i.e position and orientation, which is compared with the pose provided by a Ground-Truth system also developed for this work with the aid of the ArUco library, thus realizing the accuracy of the developed algorithm.
The developed work showed that with the use of the Pozyx system and ArUco markers it is possible to improve the robot localization, meaning that it is an adequate and effective solution for this purpose
A Survey of 3D Indoor Localization Systems and Technologies
Indoor localization has recently and significantly attracted the interest of the research community mainly due to the fact that Global Navigation Satellite Systems (GNSSs) typically fail in indoor environments. In the last couple of decades, there have been several works reported in the literature that attempt to tackle the indoor localization problem. However, most of this work is focused solely on two-dimensional (2D) localization, while very few papers consider three dimensions (3D). There is also a noticeable lack of survey papers focusing on 3D indoor localization; hence, in this paper, we aim to carry out a survey and provide a detailed critical review of the current state of the art concerning 3D indoor localization including geometric approaches such as angle of arrival (AoA), time of arrival (ToA), time difference of arrival (TDoA), fingerprinting approaches based on Received Signal Strength (RSS), Channel State Information (CSI), Magnetic Field (MF) and Fine Time Measurement (FTM), as well as fusion-based and hybrid-positioning techniques. We provide a variety of technologies, with a focus on wireless technologies that may be utilized for 3D indoor localization such as WiFi, Bluetooth, UWB, mmWave, visible light and sound-based technologies. We critically analyze the advantages and disadvantages of each approach/technology in 3D localization
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