Minimum neighbour and extended Kalman filter estimator: a practical distributed channel assignment scheme for dense wireless local area networks

Dense deployments of wireless local area networks (WLANs) are becoming a norm in many cities around the world. However, increased interference and traf���c demands can severely limit the aggregate throughput achievable unless an effective channel assignment scheme is used. In this work, a simple and effective distributed channel assignment (DCA) scheme is proposed. It is shown that in order to maximise throughput, each access point (AP) simply chooses the channel with the minimum number of active neighbour nodes (i.e. nodes associated with neighbouring APs that have packets to send). However, application of such a scheme to practice depends critically on its ability to estimate the number of neighbour nodes in each channel, for which no practical estimator has been proposed before. In view of this, an extended Kalman ���lter (EKF) estimator and an estimate of the number of nodes by AP are proposed. These not only provide fast and accurate estimates but can also exploit channel switching information of neighbouring APs. Extensive packet level simulation results show that the proposed minimum neighbour and EKF estimator (MINEK) scheme is highly scalable and can provide signi���cant throughput improvement over other channel assignment schemes.

Sistema visual para Ground Truth de sistemas aut��nomos

Este trabalho visa contribuir para o desenvolvimento de um sistema de vis��o multi-c��mara para determina����o da localiza����o, atitude e seguimento de m��ltiplos objectos, para ser utilizado na unidade de rob��tica do INESCTEC, e resulta da necessidade de ter informa����o externa exacta que sirva de refer��ncia no estudo, caracteriza����o e desenvolvimento de algoritmos de localiza����o, navega����o e controlo de v��rios sistemas aut��nomos. Com base na caracteriza����o dos ve��culos aut��nomos existentes na unidade de rob��tica do INESCTEC e na an��lise dos seus cen��rios de opera����o, foi efectuado o levantamento de requisitos para o sistema a desenvolver. Foram estudados os fundamentos te��ricos, necess��rios ao desenvolvimento do sistema, em temas relacionados com vis��o computacional, m��todos de estima����o e associa����o de dados para problemas de seguimento de m��ltiplos objectos . Foi proposta uma arquitectura para o sistema global que endere��a os v��rios requisitos identi cados, permitindo a utiliza����o de m��ltiplas c��maras e suportando o seguimento de m��ltiplos objectos, com ou sem marcadores. Foram implementados e validados componentes da arquitectura proposta e integrados num sistema para valida����o, focando na localiza����o e seguimento de m��ltiplos objectos com marcadores luminosos �� base de Light-Emitting Diodes (LEDs). Nomeadamente, os m��dulos para a identi ca����o dos pontos de interesse na imagem, t��cnicas para agrupar os v��rios pontos de interesse de cada objecto e efectuar a correspond��ncia das medidas obtidas pelas v��rias c��maras, m��todo para a determina����o da posi����o e atitude dos objectos, ltro para seguimento de m��ltiplos objectos. Foram realizados testes para valida����o e a na����o do sistema implementado que demonstram que a solu����o encontrada vai de encontro aos requisitos, e foram identi cadas as linhas de trabalho para a continua����o do desenvolvimento do sistema global.

Efficient topology estimation for large scale optical mapping

Large scale image mosaicing methods are in great demand among scientists who study di���erent aspects of the seabed, and have been fostered by impressive advances in the capabilities of underwater robots in gathering optical data from the sea���oor. Cost and weight constraints mean that lowcost Remotely operated vehicles (ROVs) usually have a very limited number of sensors. When a low-cost robot carries out a seafloor survey using a down-looking camera, it usually follows a predetermined trajectory that provides several non time-consecutive overlapping image pairs. Finding these pairs (a process known as topology estimation) is indispensable to obtaining globally consistent mosaics and accurate trajectory estimates, which are necessary for a global view of the surveyed area, especially when optical sensors are the only data source. This thesis presents a set of consistent methods aimed at creating large area image mosaics from optical data obtained during surveys with low-cost underwater vehicles. First, a global alignment method developed within a Feature-based image mosaicing (FIM) framework, where nonlinear minimisation is substituted by two linear steps, is discussed. Then, a simple four-point mosaic rectifying method is proposed to reduce distortions that might occur due to lens distortions, error accumulation and the di���culties of optical imaging in an underwater medium. The topology estimation problem is addressed by means of an augmented state and extended Kalman ���lter combined framework, aimed at minimising the total number of matching attempts and simultaneously obtaining the best possible trajectory. Potential image pairs are predicted by taking into account the uncertainty in the trajectory. The contribution of matching an image pair is investigated using information theory principles. Lastly, a di���erent solution to the topology estimation problem is proposed in a bundle adjustment framework. Innovative aspects include the use of fast image similarity criterion combined with a Minimum spanning tree (MST) solution, to obtain a tentative topology. This topology is improved by attempting image matching with the pairs for which there is the most overlap evidence. Unlike previous approaches for large-area mosaicing, our framework is able to deal naturally with cases where time-consecutive images cannot be matched successfully, such as completely unordered sets. Finally, the e���ciency of the proposed methods is discussed and a comparison made with other state-of-the-art approaches, using a series of challenging datasets in underwater scenarios

Nonlinear data assimilation in geosciences: an extremely ef���cient particle ���lter

Almost all research ���elds in geosciences use numerical models and observations and combine these using data-assimilation techniques. With ever-increasing resolution and complexity, the numerical models tend to be highly nonlinear and also observations become more complicated and their relation to the models more nonlinear. Standard data-assimilation techniques like (ensemble) Kalman ���lters and variational methods like 4D-Var rely on linearizations and are likely to fail in one way or another. Nonlinear data-assimilation techniques are available, but are only ef���cient for small-dimensional problems, hampered by the so-called ���curse of dimensionality���. Here we present a fully nonlinear particle ���lter that can be applied to higher dimensional problems by exploiting the freedom of the proposal density inherent in particle ���ltering. The method is illustrated for the three-dimensional Lorenz model using three particles and the much more complex 40-dimensional Lorenz model using 20 particles. By also applying the method to the 1000-dimensional Lorenz model, again using only 20 particles, we demonstrate the strong scale-invariance of the method, leading to the optimistic conjecture that the method is applicable to realistic geophysical problems. Copyright c 2010 Royal Meteorological Society

CHARACTERIZATION OF EXPONENTIAL DIVERGENCE OF THE KALMAN FILTER FOR TIME-VARYING SYSTEMS

This paper studies semistability of the recursive Kalman filter in the context of linear time-varying (LTV), possibly nondetectable systems with incorrect noise information. Semistability is a key property, as it ensures that the actual estimation error does not diverge exponentially. We explore structural properties of the filter to obtain a necessary and sufficient condition for the filter to be semistable. The condition does not involve limiting gains nor the solution of Riccati equations, as they can be difficult to obtain numerically and may not exist. We also compare semistability with the notions of stability and stability w.r.t. the initial error covariance, and we show that semistability in a sense makes no distinction between persistent and nonpersistent incorrect noise models, as opposed to stability. In the linear time invariant scenario we obtain algebraic, easy to test conditions for semistability and stability, which complement results available in the context of detectable systems. Illustrative examples are included.

Noise reduction in biomedical speech signal processing based on time and frequency Kalman filtering combined with spectral subtraction

The goal of this paper is to study and propose a new technique for noise reduction used during the reconstruction of speech signals, particularly for biomedical applications. The proposed method is based on Kalman filtering in the time domain combined with spectral subtraction. Comparison with discrete Kalman filter in the frequency domain shows better performance of the proposed technique. The performance is evaluated by using the segmental signal-to-noise ratio and the Itakura-Saito`s distance. Results have shown that Kalman`s filter in time combined with spectral subtraction is more robust and efficient, improving the Itakura-Saito`s distance by up to four times. (C) 2007 Elsevier Ltd. All rights reserved.

Kalman tracking linear predictor for vowel intelligibility enhancement on european portuguese HMM based speech synthesis

The recent developments on Hidden Markov Models (HMM) based speech synthesis showed that this is a promising technology fully capable of competing with other established techniques. However some issues still lack a solution. Several authors report an over-smoothing phenomenon on both time and frequencies which decreases naturalness and sometimes intelligibility. In this work we present a new vowel intelligibility enhancement algorithm that uses a discrete Kalman filter (DKF) for tracking frame based parameters. The inter-frame correlations are modelled by an autoregressive structure which provides an underlying time frame dependency and can improve time-frequency resolution. The system���s performance has been evaluated using objective and subjective tests and the proposed methodology has led to improved results.

Structural time series models and the Kalman filter: A concise review

The continued increase in availability of economic data in recent years and, more importantly, the possibility to construct larger frequency time series, have fostered the use (and development) of statistical and econometric techniques to treat them more accurately. This paper presents an exposition of structural time series models by which a time series can be decomposed as the sum of a trend, seasonal and irregular components. In addition to a detailled analysis of univariate speci fications we also address the SUTSE multivariate case and the issue of cointegration. Finally, the recursive estimation and smoothing by means of the Kalman filter algorithm is described taking into account its different stages, from initialisation to parameter s estimation.

A low dimensional Kalman filter for systems with lagged observables

This note describes how the Kalman filter can be modified to allow for thevector of observables to be a function of lagged variables without increasing the dimensionof the state vector in the filter. This is useful in applications where it is desirable to keepthe dimension of the state vector low. The modified filter and accompanying code (whichnests the standard filter) can be used to compute (i) the steady state Kalman filter (ii) thelog likelihood of a parameterized state space model conditional on a history of observables(iii) a smoothed estimate of latent state variables and (iv) a draw from the distribution oflatent states conditional on a history of observables.

Potencialidad de la modelizaci��n state-space y el filtro de Kalman para el an��lisis regional : una aplicaci��n para el ��ndice de actividad industrial

El d��ficit existente a nuestro pa��s con respecto a la disponibilidad de indicadores cuantitativos con los que llevar a t��rmino un an��lisis coyuntural de la actividad industrial regional ha abierto un debate centrado en el estudio de cu��l es la metodolog��a m��s adecuada para elaborar indicadores de estas caracter��sticas. Dentro de este marco, en este trabajo se presentan las principales conclusiones obtenidas en anteriores estudios (Clar, et. al., 1997a, 1997b y 1998) sobre la idoneidad de extender las metodolog��as que actualmente se est��n aplicando a las regiones espa��olas para elaborar indicadores de la actividad industrial mediante m��todos indirectos. Estas conclusiones llevan a plantear una estrategia distinta a las que actualmente se vienen aplicando. En concreto, se propone (siguiendo a Israilevich y Kuttner, 1993) un modelo de variables latentes para estimar el indicador de la producci��n industrial regional. Este tipo de modelo puede especificarse en t��rminos de un modelo statespace y estimarse mediante el filtro de Kalman. Para validar la metodolog��a propuesta se estiman unos indicadores de acuerdo con ella para tres de las cuatro regiones espa��olas que disponen d��un ��ndice de Producci��n Industrial (IPI) elaborado mediante el m��todo directo (Andaluc��a, Asturias y el Pa��s Vasco) y se comparan con los IPIs publicados (oficiales). Los resultados obtenidos muestran el buen comportamiento de l��estrategia propuesta, abriendo as�� una l��nea de trabajo con la que subsanar el d��ficit al que se hac��a referencia anteriormente

Potencialidad de la modelizaci��n state-space y el filtro de Kalman para el an��lisis regional : una aplicaci��n para el ��ndice de actividad industrial

El d��ficit existente a nuestro pa��s con respecto a la disponibilidad de indicadores cuantitativos con los que llevar a t��rmino un an��lisis coyuntural de la actividad industrial regional ha abierto un debate centrado en el estudio de cu��l es la metodolog��a m��s adecuada para elaborar indicadores de estas caracter��sticas. Dentro de este marco, en este trabajo se presentan las principales conclusiones obtenidas en anteriores estudios (Clar, et. al., 1997a, 1997b y 1998) sobre la idoneidad de extender las metodolog��as que actualmente se est��n aplicando a las regiones espa��olas para elaborar indicadores de la actividad industrial mediante m��todos indirectos. Estas conclusiones llevan a plantear una estrategia distinta a las que actualmente se vienen aplicando. En concreto, se propone (siguiendo a Israilevich y Kuttner, 1993) un modelo de variables latentes para estimar el indicador de la producci��n industrial regional. Este tipo de modelo puede especificarse en t��rminos de un modelo statespace y estimarse mediante el filtro de Kalman. Para validar la metodolog��a propuesta se estiman unos indicadores de acuerdo con ella para tres de las cuatro regiones espa��olas que disponen d��un ��ndice de Producci��n Industrial (IPI) elaborado mediante el m��todo directo (Andaluc��a, Asturias y el Pa��s Vasco) y se comparan con los IPIs publicados (oficiales). Los resultados obtenidos muestran el buen comportamiento de l��estrategia propuesta, abriendo as�� una l��nea de trabajo con la que subsanar el d��ficit al que se hac��a referencia anteriormente

Augmented state Kalman filtering for AUV navigation

Addresses the problem of estimating the motion of an autonomous underwater vehicle (AUV), while it constructs a visual map ("mosaic" image) of the ocean floor. The vehicle is equipped with a down-looking camera which is used to compute its motion with respect to the seafloor. As the mosaic increases in size, a systematic bias is introduced in the alignment of the images which form the mosaic. Therefore, this accumulative error produces a drift in the estimation of the position of the vehicle. When the arbitrary trajectory of the AUV crosses over itself, it is possible to reduce this propagation of image alignment errors within the mosaic. A Kalman filter with augmented state is proposed to optimally estimate both the visual map and the vehicle position

Estudi comparatiu dels filtres de Kalman i IMM per a navegaci��

Els sistemes h��brids de navegaci�� integren mesures de posici�� i velocitat provinents de sat��l��lits (GPS) i d���unitats de mesura inercials (IMU).Les dades d���aquests sensors s���han de fusionar i suavitzar, i per a aquest prop��sit existeixen diversos algorismes de filtratge, que tracten les dades conjuntament o per separat. En aquest treball s���han codificat en Matlab els algorismes dels filtres de Kalman i IMM, i s���han comparat les seves prestacions en diverses traject��ries d���un vehicle. S���han avaluat quantitativament els errors dels dos filtres, i s���han sintonitzat els seus par��metres per a minimitzar aquests errors. Amb una correcta sintonia dels filtres, s���ha comprovat que el filtre IMM ��s superior al filtre de Kalman, tant per maniobres brusques com per maniobres suaus, malgrat que la complexitat i el temps de c��lcul requerit s��n majors.