10 research outputs found
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
The Nonsequential Fusion Method for Localization from Unscented Kalman Filter by Multistation Array Buoys
Based on special features of array buoy and the research field of location and tracking of underwater target, the research combines the highly adaptive nonlinear filtering algorithm unscented Kalman filter with the nonlinear programming of multistation array buoy positioning system. In accordance with the model of nonsequential target location, the research utilizes Unscented Transformation to update the measuring error and covariance matrix of state error, aiming at estimating the filtering of state variable and acquiring the object’s current state of motion. The research analyzes the positioning performance of algorithm, pursuit path, astringency, and other performance indexes of target-relevant parameter through numerical simulation experiment. From the result, the conclusion that multistation array buoy can complete the task of tracing target track very well can be reached, which provides theoretical foundation for putting the algorithm into engineering practice
Localization of Non-Linearly Modeled Autonomous Mobile Robots Using Out-of-Sequence Measurements
This paper presents a state of the art of the estimation algorithms dealing with Out-of-Sequence (OOS) measurements for non-linearly modeled systems. The state of the art includes a critical analysis of the algorithm properties that takes into account the applicability of these techniques to autonomous mobile robot navigation based on the fusion of the measurements provided, delayed and OOS, by multiple sensors. Besides, it shows a representative example of the use of one of the most computationally efficient approaches in the localization module of the control software of a real robot (which has non-linear dynamics, and linear and non-linear sensors) and compares its performance against other approaches. The simulated results obtained with the selected OOS algorithm shows the computational requirements that each sensor of the robot imposes to it. The real experiments show how the inclusion of the selected OOS algorithm in the control software lets the robot successfully navigate in spite of receiving many OOS measurements. Finally, the comparison highlights that not only is the selected OOS algorithm among the best performing ones of the comparison, but it also has the lowest computational and memory cost
Leader-assisted localization approach for a heterogeneous multi-robot system
This thesis presents the design, implementation, and validation of a novel leader assisted localization framework for a heterogeneous multi-robot system (MRS) with sensing and communication range constraints. It is assumed that the given heterogeneous MRS has a more powerful robot (or group of robots) with accurate self localization capabilities (leader robots) while the rest of the team (child robots), i.e. less powerful robots, is localized with the assistance of leader robots and inter-robot observation between teammates. This will eventually pose a condition that the child
robots should be operated within the sensing and communication range of leader
robots. The bounded navigation space therefore may require added algorithms to
avoid inter-robot collisions and limit robots’ maneuverability. To address this limitation,
first, the thesis introduces a novel distributed graph search and global pose composition
algorithm to virtually enhance the leader robots’ sensing and communication
range while avoiding possible double counting of common information. This allows
child robots to navigate beyond the sensing and communication range of the leader
robot, yet receive localization services from the leader robots. A time-delayed measurement
update algorithm and a memory optimization approach are then integrated
into the proposed localization framework. This eventually improves the robustness
of the algorithm against the unknown processing and communication time-delays associated
with the inter-robot data exchange network. Finally, a novel hierarchical sensor fusion architecture is introduced so that the proposed localization scheme can
be implemented using inter-robot relative range and bearing measurements.
The performance of the proposed localization framework is evaluated through a series
of indoor experiments, a publicly available multi-robot localization and mapping
data-set and a set of numerical simulations. The results illustrate that the proposed
leader-assisted localization framework is capable of establishing accurate and nonoverconfident
localization for the child robots even when the child robots operate
beyond the sensing and communication boundaries of the leader robots
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
Recommended from our members
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