Sequential bayesian filtering for spatial arrival time estimation

Locating and tracking a source in an ocean environment as well as estimating environmental parameters of a sound propagation medium is of utmost importance in underwater acoustics. Matched field processing is often the method of choice for the estimation of such parameters. This approach, based on full field calculations, is computationally intensive and sensitive to assumptions on the structure of the environment. As an alternative, methods that use only select features of the acoustic field for source localization and environmental inversion have been proposed. The focus here is on inversion using arrival times of identified paths within recorded time-series. After a short study of a linearization techniques employing such features and numerical issues on their implementation, we turn our attention to the need for accurate extraction of arrival times for accurate estimation. We develop a particle filtering approach that treats arrival times as targets , dynamically modeling their location at arrays of spatially separated receivers. Using Monte Carlo simulations, we perform an evaluation of our method and compare it to conventional Maximum Likelihood (ML) estimation. The comparison demonstrates an advantage in using the proposed approach, which can be employed as a pre-inversion tool for minimization and quantification of uncertainty in arrival time estimation

Wi-Fi based people tracking in challenging environments

People tracking is a key building block in many applications such as abnormal activity detection, gesture recognition, and elderly persons monitoring. Video-based systems have many limitations making them ineffective in many situations. Wi-Fi provides an easily accessible source of opportunity for people tracking that does not have the limitations of video-based systems. The system will detect, localise, and track people, based on the available Wi-Fi signals that are reflected from their bodies. Wi-Fi based systems still need to address some challenges in order to be able to operate in challenging environments. Some of these challenges include the detection of the weak signal, the detection of abrupt people motion, and the presence of multipath propagation. In this thesis, these three main challenges will be addressed. Firstly, a weak signal detection method that uses the changes in the signals that are reflected from static objects, to improve the detection probability of weak signals that are reflected from the person’s body. Then, a deep learning based Wi-Fi localisation technique is proposed that significantly improves the runtime and the accuracy in comparison with existing techniques. After that, a quantum mechanics inspired tracking method is proposed to address the abrupt motion problem. The proposed method uses some interesting phenomena in the quantum world, where the person is allowed to exist at multiple positions simultaneously. The results show a significant improvement in reducing the tracking error and in reducing the tracking delay

Position estimation for IR-UWB systems using compressive sensing

Recently, a growing interest in precise indoor wireless locating systems has been observed. Indoor environments are typically complex wireless propagation channels with numerous multi-paths created by closely spaced scattering objects. The ability to resolve these multi-paths is very important for good ranging resolution and positioning accuracy. Impulse-Radio Ultra-Wideband (IRUWB) is a promising technology to fulfill these requirements in harsh indoor propagation environments due to its great time resolution and immunity to multipath fading. One of the major IRUWB signal processing challenges is the high sampling demands of IR-UWB digital receivers, which greatly elevates the cost and power consumption of IR-UWB systems . Compressive Sensing provides a solution by allowing them to sample IR-UWB signals at a lower rate than the Nyquist sampling limit. The CS approach relies on the fact sparse representations are possible in the localization context. Basically two sparsity patterns can be exploited: Firstly, transmitting an ultra-short pulse through a multipath UWB channel leads to a received UWB signal that can be approximated by a linear combination of a few atoms from a pre-defined dictionary, yielding thus a sparse representation of the received UWB signal. Secondly, the inherent spatial sparsity of scene can be introduced through the use of an overcomplete basis or dictionary that enables to jointly evaluate all multiple location hypothesis. In this degree thesis, three novel data-acquisition and positioning methods exploiting different sparse representations for IR-UWB signals under challenging indoor environments are presented. Essentially, through the formulation of sparsity-based reconstruction techniques it is viable to localize targets while reducing the computational load and sampling requirements. Their performance is assessed and compared under the framework of the IEE.802.15.14a channel models, which is a standard developed specifically for UWB wireless positioning.Recientemente, se ha observado un interés creciente en los sistemas de localización pasiva inalámbrica para edificios interiores como oficinas o naves industriales. Típicamente, los ambientes de interiores son canales de propagación inalámbricos complejos con numerosas reflexiones creadas por objetos dispersivos muy próximos entre sí. La capacidad de resolver estos múltiples caminos es muy importante para una buena resolución de alcance y precisión de posicionamiento. Impulso-radio de banda ultra-ancha (UWB-IR) es una tecnología prometedora para cumplir con estos requisitos en entornos de propagación interiores debido a su gran resolución temporal y la inmunidad al desvanecimiento por múltiples caminos. Uno de los principales retos de procesamiento de señales IR-UWB es la alta demanda de muestreo de receptores digitales IRUWB, lo que eleva considerablemente el costo y el consumo de energía de los sistemas IR-UWB. Compressive Sensing proporciona una solución que permite muestrear señales IR-UWB a un ritmo menor que el límite de muestreo propuesto por Nyquist. El enfoque de este problema con Compressive Sensing se basa en el hecho de que representaciones dispersas son posibles en el contexto de la localización. Básicamente dos patrones de dispersión pueden ser explotados: En primer lugar, la transmisión de un pulso ultra corto, través de un canal de banda ancha donde la señal experimenta trayectos múltiples, conduce a una señal de UltraWideband recibida que puede ser aproximada por una combinación lineal de unos pocos átomos de un diccionario predefinido, obteniéndose así una representación dispersa de la señal de UWB recibida. En segundo lugar, la escasez de objetivos a localizar de la escena se puede utilizar mediante el uso de un diccionario sobre-completo que permita evaluar conjuntamente las múltiples hipótesis de ubicación en un escenario bidimensional, adquiriendo así una representación dispersa, con pocos elementos. En este proyecto final de carrera, se presentan tres nuevos métodos de adquisición de datos y posicionamiento que explotan diferentes representaciones dispersas para señales IR-UWB bajo ambientes interiores. En esencia se plantea, mediante la formulación de técnicas de reconstrucción de Compressive Sensing, que es viable localizar objetivos y al mismo tiempo reducir los requisitos de carga computacional y altos ritmos de muestreo. El rendimiento de los algoritmos propuestos se evalúa y se compara en el marco de los modelos de canal IEE.802.15.14a, que es un estándar desarrollado específicamente para el posicionamiento inalámbrico en sistemas UltraWideband.Recentment, s'ha observat un interès creixent en els sistemes de localització passiva sense fil per a edificis interiors com oficines o naus industrials. Típicament, els ambients d'interiors són canals de propagació complexos amb nombroses reflexions creades per objectes dispersius molt pròxims entre si. La capacitat de resoldre aquests múltiples camins és molt important per a una bona resolució d'abast i precisió de posicionament. Impuls-ràdio de banda ultra-ampla (UWB-IR) és una tecnologia prometedora per complir amb aquests requisits en entorns de propagació interiors a causa de la seva gran resolució temporal i la immunitat al esvaniment per múltiples camins. Un dels principals reptes de processament de senyals IR-UWB és l'alta demanda de mostreig dels receptors digitals IR-UWB, el que eleva considerablement el cost i el consum d'energia dels sistemes IR-UWB. Compressive Sensing proporciona una solució en la qual permet mostrejar senyals IR-UWB a un ritme menor que el límit de mostreig proposat per Nyquist. L'enfocament d'aquest problema amb Compressive Sensing es basa en el fet que representacions disperses són possibles en el context de la localització. Bàsicament dos patrons de dispersió poden ser explotats: En primer lloc, la transmissió d'un pols de molt poca duració a través d'un canal de banda ample on la senyal experimenta múltiples trajectes, això condueix a una senyal de UltraWideband rebuda que pot ser aproximada per una combinació lineal d'uns pocs àtoms d'un diccionari predefinit, obtenint-se així una representació dispersa. En segon lloc, l'escassetat de objectius a localitzar en l?escena es pot utilitzar mitjançant l'ús d'un diccionari sobre-complet que permeti avaluar conjuntament les múltiples hipòtesis d'ubicació en un escenari bidimensional, adquirint així una representació dispersa. En aquest projecte final de carrera, es presenten tres nous mètodes d'adquisició de dades i posicionament que exploten diferents representacions disperses per senyals IR-UWB sota ambients interiors. En essència es planteja, mitjançant la formulació de tècniques de reconstrucció de Compressive Sensing, que és viable localitzar objectius i al mateix temps reduir els requisits de càrrega computacional i alts ritmes de mostreig. El rendiment dels algoritmes proposats s'avalua i es comparen en el marc dels models de canal IEE.802.15.14a, que és un estàndard desenvolupat específicament per al posicionament sense fil en sistemes UltraWideband

Least squares-based iterative identification methods for linear-in-parameters systems using the decomposition technique

By extending the least squares-based iterative (LSI) method, this paper presents a decomposition-based LSI (D-LSI) algorithm for identifying linear-in-parameters systems and an interval-varying D-LSI algorithm for handling the identification problems of missing-data systems. The basic idea is to apply the hierarchical identification principle to decompose the original system into two fictitious sub-systems and then to derive new iterative algorithms to estimate the parameters of each sub-system. Compared with the LSI algorithm and the interval-varying LSI algorithm, the decomposition-based iterative algorithms have less computational load. The numerical simulation results demonstrate that the proposed algorithms work quite well

Classification and modeling of power line noise using machine learning techniques

A thesis submitted in ful lment of the requirements for the degree of Doctor of Philosophy in the School of Electrical and Information Engineering Faculty of Engineering and Built Environment June 2017The realization of robust, reliable and e cient data transmission have been the theme of recent research, most importantly in real channel such as the noisy, fading prone power line communication (PLC) channel. The focus is to exploit old techniques or create new techniques capable of improving the transmission reliability and also increasing the transmission capacity of the real communication channels. Multi-carrier modulation scheme such as Orthogonal Frequency Division Multiplexing (OFDM) utilizing conventional single-carrier modulation is developed to facilitate a robust data transmission, increasing transmission capacity (e cient bandwidth usage) and further reducing design complexity in PLC systems. On the contrary, the reliability of data transmission is subjected to several inhibiting factors as a result of the varying nature of the PLC channel. These inhibiting factors include noise, perturbation and disturbances. Contrary to the Additive White Gaussian noise (AWGN) model often assumed in several communication systems, this noise model fails to capture the attributes of noise encountered on the PLC channel. This is because periodic noise or random noise pulses injected by power electronic appliances on the network is a deviation from the AWGN. The nature of the noise is categorized as non-white non-Gaussian and unstable due to its impulsive attributes, thus, it is labeled as Non-additive White Gaussian Noise (NAWGN). These noise and disturbances results into long burst errors that corrupts signals being transmitted, thus, the PLC is labeled as a horrible or burst error channel. The e cient and optimal performance of a conventional linear receiver in the white Gaussian noise environment can therefore be made to drastically degrade in this NAWGN environment. Therefore, transmission reliability in such environment can be greatly enhanced if we know and exploit the knowledge of the channel's statistical attributes, thus, the need for developing statistical channel model based on empirical data. In this thesis, attention is focused on developing a recon gurable software de ned un-coded single-carrier and multicarrier PLC transceiver as a tool for realizing an optimized channel model for the narrowband PLC (NB-PLC) channel. First, a novel recon gurable software de ned un-coded single-carrier and multi-carrier PLC transceiver is developed for real-time NB-PLC transmission. The transceivers can be adapted to implement di erent waveforms for several real-time scenarios and performance evaluation. Due to the varying noise parameters obtained from country to country as a result of the dependence of noise impairment on mains voltages, topology of power line, place and time, the developed transceivers is capable of facilitating constant measurement campaigns to capture these varying noise parameters before statistical and mathematically inclined channel models are derived. Furthermore, the single-carrier (Binary Phase Shift Keying (BPSK), Di erential BPSK (DBPSK), Quadrature Phase Shift Keying (QPSK) and Di erential QPSK (DQPSK)) PLC transceiver system developed is used to facilitate a First-Order semi-hidden Fritchman Markov modeling (SHFMM) of the NB-PLC channel utilizing the e cient iterative Baum- Welch algorithm (BWA) for parameter estimation. The performance of each modulation scheme is evaluated in a mildly and heavily disturbed scenarios for both residential and laboratory site considered. The First-Order estimated error statistics of the realized First- Order SHFMM have been analytically validated in terms of performance metrics such as: log-likelihood ratio (LLR), error-free run distribution (EFRD), error probabilities, mean square error (MSE) and Chi-square ( 2) test. The reliability of the model results is also con rmed by an excellent match between the empirically obtained error sequence and the SHFMM regenerated error sequence as shown by the error-free run distribution plot. This thesis also reports a novel development of a low cost, low complexity Frequency-shift keying (FSK) - On-o keying (OOK) in-house hybrid PLC and VLC system. The functionality of this hybrid PLC-VLC transceiver system was ascertained at both residential and laboratory site at three di erent times of the day: morning, afternoon and evening. A First and Second-Order SHFMM of the hybrid system is realized. The error statistics of the realized First and Second-Order SHFMMs have been analytically validated in terms of LLR, EFRD, error probabilities, MSE and Chi-square ( 2). The Second-Order SHFMMs have also been analytically validated to be superior to the First-Order SHFMMs although at the expense of added computational complexity. The reliability of both First and Second-Order SHFMM results is con rmed by an excellent match between the empirical error sequences and SHFMM re-generated error sequences as shown by the EFRD plot. In addition, the multi-carrier (QPSK-OFDM, Di erential QPSK (DQPSK)-OFDM) and Di erential 8-PSK (D8PSK)-OFDM) PLC transceiver system developed is used to facilitate a First and Second-Order modeling of the NB-PLC system using the SHFMM and BWA for parameter estimation. The performance of each OFDM modulation scheme in evaluated and compared taking into consideration the mildly and heavily disturbed noise scenarios for the two measurement sites considered. The estimated error statistics of the realized SHFMMs have been analytically validated in terms of LLR, EFRD, error probabilities, MSE and Chi-square ( 2) test. The estimated Second-Order SHFMMs have been analytically validated to be outperform the First-Order SHFMMs although with added computational complexity. The reliability of the models is con rmed by an excellent match between the empirical data and SHFMM generated data as shown by the EFRD plot. The statistical models obtained using Baum-Welch to adjust the parameters of the adopted SHFMM are often locally maximized. To solve this problem, a novel Metropolis-Hastings algorithm, a Bayesian inference approach based on Markov Chain Monte Carlo (MCMC) is developed to optimize the parameters of the adopted SHFMM. The algorithm is used to optimize the model results obtained from the single-carrier and multi-carrier PLC systems as well as that of the hybrid PLC-VLC system. Consequently, as deduced from the results, the models obtained utilizing the novel Metropolis-Hastings algorithm are more precise, near optimal model with parameter sets that are closer to the global maxima. Generally, the model results obtained in this thesis are relevant in enhancing transmission reliability on the PLC channel through the use of the models to improve the adopted modulation schemes, create adaptive modulation techniques, develop and evaluate forward error correction (FEC) codes such as a concatenation of Reed-Solomon and Permutation codes and other robust codes suitable for exploiting and mitigating noise impairments encountered on the low voltage NB-PLC channel. Furthermore, the recon gurable software de ned NB-PLC transceiver test-bed developed can be utilized for future measurement campaign as well as adapted for multiple-input and multiple-output (MIMO) PLC applications.MT201