Fractionally sampled decorrelating detectors for time-varying rayleigh fading CDMA channels

In this dissertation, we propose novel decorrelating multiuser detectors in DSCDMA time-varying frequency-nonselective and frequency-selective fading channels and analyze their performance. We address the common shortcomings of existing multiuser detectors in a mobile environment, such as detector complexity and the error floor. An analytical approach is employed almost exclusively and Monte Carlo simulation is used to confirm the theoretical results. Practical channel models, such as Jakes\u27 and Markovian, are adopted in the numerical examples. The proposed detectors are of the decorrelating type and utilize fractional sampling to simultaneously achieve two goals: (1) the novel realization of a decorrelator with lower computational complexity and shorter processing latency; and (2) the significant reduction of the probability of error floor associated with time-varying fading. The analysis of the impact of imperfect power control on IS-95 multiple access interference is carried out first and the ineffectiveness of IS-95 power control in a mobile radio environment is demonstrated. Fractionally-spaced bit-by-bit decorrelator structures for the frequency-nonselective and frequency-selective channels are then proposed. The matrix singularity problem associated with decorrelation is also addressed, and its solution is suggested. A decorrelating receiver employing differentially coherent detection for an asynchronous CDMA, frequency-nonselective time-varying Rayleigh fading channel is proposed. A maximum likelihood detection principle is applied at the fractionally spaced decorrelator output, resulting in a significantly reduced error floor. For coherent detection, a novel single-stage and two-stage decision feedback (DF) maximum a posteriori (MAP) channel estimator is proposed. These estimators are applicable to a channel with an arbitrary spaced-time correlation function. The fractionally-spaced decorrelating detector is then modified and extended to a frequency-selective time-varying fading channel, and is shown to be capable of simultaneously eliminating MAI, ISI, and path cross-correlation interference. The implicit equivalent frequency diversity is exploited through multipath combining, and the effective time diversity is achieved by fractional sampling for significant performance improvement. The significance of the outcome of this research is in the design of new lower complexity multiuser detectors that do not exhibit the usual deficiencies and limitations associated with a time-varying fading and multipath CDMA mobile environment

Analysis and application of minimum variance discrete time system identification

An on-line minimum variance parameter identifier was developed which embodies both accuracy and computational efficiency. The new formulation resulted in a linear estimation problem with both additive and multiplicative noise. The resulting filter is shown to utilize both the covariance of the parameter vector itself and the covariance of the error in identification. It is proven that the identification filter is mean square covergent and mean square consistent. The MV parameter identification scheme is then used to construct a stable state and parameter estimation algorithm

Statistical models for natural sounds

It is important to understand the rich structure of natural sounds in order to solve important tasks, like automatic speech recognition, and to understand auditory processing in the brain. This thesis takes a step in this direction by characterising the statistics of simple natural sounds. We focus on the statistics because perception often appears to depend on them, rather than on the raw waveform. For example the perception of auditory textures, like running water, wind, fire and rain, depends on summary-statistics, like the rate of falling rain droplets, rather than on the exact details of the physical source. In order to analyse the statistics of sounds accurately it is necessary to improve a number of traditional signal processing methods, including those for amplitude demodulation, time-frequency analysis, and sub-band demodulation. These estimation tasks are ill-posed and therefore it is natural to treat them as Bayesian inference problems. The new probabilistic versions of these methods have several advantages. For example, they perform more accurately on natural signals and are more robust to noise, they can also fill-in missing sections of data, and provide error-bars. Furthermore, free-parameters can be learned from the signal. Using these new algorithms we demonstrate that the energy, sparsity, modulation depth and modulation time-scale in each sub-band of a signal are critical statistics, together with the dependencies between the sub-band modulators. In order to validate this claim, a model containing co-modulated coloured noise carriers is shown to be capable of generating a range of realistic sounding auditory textures. Finally, we explored the connection between the statistics of natural sounds and perception. We demonstrate that inference in the model for auditory textures qualitatively replicates the primitive grouping rules that listeners use to understand simple acoustic scenes. This suggests that the auditory system is optimised for the statistics of natural sounds

Acoustic underwater target tracking methods using autonomous vehicles

Marine ecological research related to the increasing importance which the fisheries sector has reached so far, new methods and tools to study the biological components of our oceans are needed. The capacity to measure different population and environmental parameters of marine species allows a greater knowledge of the human impact, improving exploitation strategies of these resources. For example, the displacement capacity and mobility patterns are crucial to obtain the required knowledge for a sustainable management of fisheries. However, underwater localisation is one of the main problems which must be addressed in subsea exploration, where no Global Positioning System (GPS) is available. In addition to the traditional underwater localisation systems, such as Long BaseLine (LBL) or Ultra-Short BaseLine (USBL), new methods have been developed to increase navigation performance, flexibility, and to reduce deployment costs. For example, the Range-Only and Single-Beacon (ROSB) is based on an autonomous vehicle which localises and tracks different underwater targets using slant range measurements conducted by acoustic modems. In a moving target tracking scenario, the ROSB target tracking method can be seen as a Hidden Markov Model (HMM) problem. Using Bayes' rule, the probability distribution function of the HMM states can be solved by using different filtering methods. Accordingly, this thesis presents different strategies to improve the ROSB localisation and tracking methods for static and moving targets. Determining the optimal parameters to minimize acoustic energy use and search time, and to maximize the localisation accuracy and precision, is therefore one of the discussed aspects of ROSB. Thus, we present and compare different methods under different scenarios, both evaluated in simulations and field tests. The main mathematical notation and performance of each algorithm are presented, where the best practice has been derived. From a methodology point of view, this work advances the understanding of accuracy that can be achieved by using ROSB target tracking methods with autonomous vehicles. Moreover, whereas most of the work conducted during the last years has been focused on target tracking using acoustic modems, here we also present a novel method called the Area-Only Target Tracking (AOTT). This method works with commercially available acoustic tags, thereby reducing the costs and complexity over other tracking systems. These tags do not have bidirectional communication capabilities, and therefore, the ROSB techniques are not applicable. However, this method can be used to track small targets such as jellyfish due to the reduced tag's size. The methodology behind the area-only technique is shown, and results from the first field tests conducted in Monterey Bay area, California, are also presented.La biologia marina junt amb la importància que ha adquirit el sector pesquer, fa que es requereixin noves eines per a l’estudi dels nostres oceans. La capacitat de mesurar diferents poblacions i paràmetres ambientals d’espècies marines permet millorar el coneixement de l’impacte que l’ésser humà té sobre elles, millorant-ne els mètodes d’explotació. Per exemple, la capacitat de desplaçament i els patrons de moviment són crucials per obtenir el coneixement necessari per a una explotació sostenible de les pescaries involucrades. No obstant, la localització submarina és un dels principals problemes que s’ha de resoldre en l’explotació dels recursos submarins, on el sistema de posició global (GPS) no es pot utilitzar. A part dels mètodes tradicionals de posicionament submarí, com per exemple el Long Base-Line (LBL) o el Ultra-Short Base-Line (USBL), nous mètodes han estat desenvolupats per tal de millorar la navegació, la flexibilitat, i per reduir els costos de desplegament. Per exemple, el Range-Only and Single-Beacon (ROSB) utilitza un vehicle autònom per a localitzar i seguir diferents objectius submarins mitjançant mesures de rang realitzades a partir de mòdems acústics. En un escenari on l’objectiu a seguir és mòbil, el mètode ROSB de seguiment pot ser vist com a un problema de Hidden Markov Model (HMM). Aleshores, utilitzant la regla de Bayes, la funció de distribució de probabilitat dels estats del HMM pot ser solucionat utilitzant diferents mètodes de filtratge. Per tant, s’estudien diferents estratègies per millorar el sistema de localització i seguiment basat en ROSB, tant per objectius estàtics com mòbils. En aquesta tesis, presentem i comparem diferents mètodes utilitzant diferents escenaris, els quals s’han avaluat tant en simulacions com en proves de camp reals. A més, es presenten les principals notacions matemàtiques de cada algoritme i les millors pràctiques a utilitzar. Per tant, des d’un punt de vista metodològic, aquest treball fa un pas endavant en el coneixement de l’exactitud que es pot assolir utilitzant els mètodes de localització i seguiment d’espècies mitjançant algoritmes ROSB i vehicles autònoms. A més a més, mentre molts dels treballs realitzant durant els últims anys es centren en l’ús de mòdems acústics per al seguiment d’objectius submarins, en aquesta tesis es presenta un innovador mètode anomenat Area-Only Target Tracking (AOTT). Aquest sistema utilitza petites etiquetes acústiques comercials (tag), la qual cosa, redueix el cost i la complexitat en comparació amb els altres mètodes. Addicionalment, gràcies a l’ús d’aquests tags de dimensions reduïdes, aquest sistema permet seguir espècies marines com les meduses. La metodologia utilitzada per el mètode AOTT es mostra en aquesta tesis, on també es presenten els primers experiments realitzats a la badia de Monterey a Califòrnia

Smart Passive Localization Using Time Difference of Arrival

A smart passive localization system using time difference of arrival (TDoA) measurements is designed and analyzed with the goal of providing the position information for the construction of frequency allocation maps

Adaptive filtering applications to satellite navigation

PhDDifferential Global Navigation Satellite Systems employ the extended Kalman filter to estimate the reference position error. High accuracy integrated navigation systems have the ability to mix traditional inertial sensor outputs with navigation satellite based position information and can be used to develop high accuracy landing systems for aircraft. This thesis considers a host of estimation problems associated with aircraft navigation systems that currently rely on the extended Kalman filter and proposes to use a nonlinear estimation algorithm, the unscented Kalman filter (UKF) that does not rely on Jacobian linearisation. The objective is to develop high accuracy positioning algorithms to facilitate the use of GNSS or DGNSS for aircraft landing. Firstly, the position error in a typical satellite navigation problem depends on the accuracy of the orbital ephemeris. The thesis presents results for the prediction of the orbital ephemeris from a customised navigation satellite receiver's data message. The SDP4/SDP8 algorithms and suitable noise models are used to establish the measured data. Secondly, the differential station common mode position error not including the contribution due to errors in the ephemeris is usually estimated by employing an EKF. The thesis then considers the application of the UKF to the mixing problem, so as to facilitate the mixing of measurements made by either a GNSS or a DGNSS and a variety of low cost or high-precision INS sensors. Precise, adaptive UKFs and a suitable nonlinear propagation method are used to estimate the orbit ephemeris and the differential position and the navigation filter mixing errors. The results indicate the method is particularly suitable for estimating the orbit ephemeris of navigation satellites and the differential position and navigation filter mixing errors, thus facilitating interoperable DGNSS operation for aircraft landing

A Safety System based on Bluetooth Low Energy (BLE) to prevent the misuse of Personal Protection Equipment (PPE) in construction

In this paper we address the issue of safety in the use of Personal Protection Equipment (PPE) in construction, industrial, or similar sites where power tools are used. We propose a novel solution that can control actively the power of the tool depending on the worker–tool distance. It is based on RSSI information transmitted by BLE devices arranged in a particular rig, combined with a Bayesian distance estimator. Such an approach minimizes the required instrumentation of the workplace and also the number of configuration parameters; therefore it enables a wide range of applications. Our aim is not only to signal risky situations caused by the misuse of the PPE (either due to its bad fitting or a wrong distance to the tool), but to intervene in a fast and robust way to avoid the safety risk. This solution is built upon previous results on the statistically sound measurement of distances and closeness in construction sites. Here, we contribute with a thorough analysis of collocating several BLE transmitters near orthogonally, which reduces interferences while avoiding the cost of more advanced technologies. We study how many transmitters are needed and what parameters are the best in the Bayesian filter for the optimal performance of the system. Real experiments with a prototype have been conducted in a construction workshop where a person operates a miter saw. The results show how the correct use of the PPE (an earmuff equipped with the BLE transmitters) can be inferred from the distance estimation in a robust and reliable way.This research received funding from Plan Propio-Universidad de Málaga and it is associated to the Proyecto Puente “Integración de dispositivos basados en el paradigma loT para la mejora de seguridad laboral en proyectos de contrucción (IoTcons)”. Funding for open access charge: Universidad de Málaga / CBU