The possibilities for positioning in cellular networks has increased over time, pushed by increased needs for location based products and services for a variety of purposes. It all started with rough position estimates based on timing measurements and sector information available in the global system for mobile communication (gsm), and today there is an increased standardization effort to provide more position relevant measurements in cellular communication systems to improve on localization accuracy and availability. A first purpose of this thesis is to survey recent efforts in the area and their potential for localization. The rest of the thesis then investigates three particular aspects, where the focus is on timing measurements. How can these be combined in the best way in long term evolution (lte), what is the potential for the new narrow-band communication links for localization, and can the timing measurement error be more accurately modeled? The first contribution concerns a narrow-band standard in lte intended for internet of things (iot) devices. This lte standard includes a special position reference signal sent synchronized by all base stations (bs) to all iot devices. Each device can then compute several pair-wise time differences that corresponds to hyperbolic functions. Using multilateration methods the intersection of a set of such hyperbolas can be computed. An extensive performance study using a professional simulation environment with realistic user models is presented, indicating that a decent position accuracy can be achieved despite the narrow bandwidth of the channel. The second contribution is a study of how downlink measurements in lte can be combined. Time of flight (tof) to the serving bs and time difference of arrival (tdoa) to the neighboring bs are used as measurements. From a geometrical perspective, the position estimation problem involves computing the intersection of a circle and hyperbolas, all with uncertain radii. We propose a fusion framework for both snapshot estimation and filtering, and evaluate with both simulated and experimental field test data. The results indicate that the position accuracy is better than 40 meters 95% of the time. A third study in the thesis analyzes the statistical distribution of timing measurement errors in lte systems. Three different machine learning methods are applied to the experimental data to fit Gaussian mixture distributions to the observed measurement errors. Since current positioning algorithms are mostly based on Gaussian distribution models, knowledge of a good model for the measurement errors can be used to improve the accuracy and robustness of the algorithms. The obtained results indicate that a single Gaussian distribution is not adequate to model the real toa measurement errors. One possible future study is to further develop standard algorithms with these models.

Estimating the frequency of a periodic signal, or tracking the time-varying frequency of an almost periodic signal, is an important problem that is well studied in literature. This thesis focuses on two subproblems where contributions can be made to the existing theory: frequency tracking methods and measurements containing outliers. Maximum-likelihood-based frequency estimation methods are studied, focusing on methods which can handle outliers in the measurements. Katkovnik’s frequency estimation method is generalized to real and harmonic signals, and a new method based on expectation-maximization is proposed. The methods are compared in a simulation study in which the measurements contain outliers. The proposed methods are compared with the standard periodogram method. Recursive Bayesian methods for frequency tracking are studied, focusing on the Rao-Blackwellized point mass filter (RBPMF). Two reformulations of the RBPMF aiming to reduce computational costs are proposed. Furthermore, the technique of variational approximate Rao-Blackwellization is proposed, which allows usage of a Student’s t distributed measurement noise model. This enables recursive frequency tracking methods to handle outliers using heavy-tailed noise models in Rao-Blackwellized filters such as the RBPMF. A simulation study illustrates the performance of the methods when outliers occur in the measurement noise. The framework above is applied to and studied in detail in two applications. The first application is on frequency tracking of engine sound. Microphone measurements are used to track the frequency of Doppler-shifted variants of the engine sound of a vehicle moving through an area. These estimates can be used to compute the speed of the vehicle. Periodogram-based methods and the RBPMF are evaluated on simulated and experimental data. The results indicate that the RBPMF has lower rmse than periodogram-based methods when tracking fast changes in the frequency. The second application relates to frequency tracking of wheel vibrations, where a car has been equipped with an accelerometer. The accelerometer measurements are used to track the frequency of the wheel axle vibrations, which relates to the wheel rotational speed. The velocity of the vehicle can then be estimated without any other sensors and without requiring integration of the accelerometer measurements. In situations with high signal-to-noise ratio (SNR), the methods perform well. To remedy situations when the methods perform poorly, an accelerometer input is introduced to the formulation. This input is used to predict changes in the frequency for short time intervals. / Periodiska signaler förekommer ofta i praktiken. I många tillämpningar är det intressant att försöka skatta frekvensen av dessa periodiska signaler, eller vibrationer, genom mätningar av dem. Detta kallas för frekvensskattning eller frekvensföljning beroende på om frekvensen är konstant eller varierar över tid. Två tillämpningar studeras i denna licentiatavhandling. Målet i båda tillämpningarna är att skatta hastigheten på fordon. Den första tillämpningen handlar om att följa frekvensen av ett fordons motorljud, när fordonet kör genom ett område där mikrofoner har blivit utplacerade. Man kan skatta ett fordons hastighet från motorljudet, vars frekvens beror på Dopplereffekten. Denna avhandling undersöker förbättrad följning av denna frekvens, vilket förbättrar skattningen av hastigheten. Två olika sätt för frekvensföljning används. Ett sätt är att anta att frekvensen är konstant inom korta tidsintervall och räkna ut en skattning av frekvensen. Ett annat sätt är att använda en matematisk modell som tar hänsyn till att frekvensen varierar över tid, och försöka följa den. För detta syfte föreslås det Rao-Blackwelliserade punktmassefiltret. Det är en metod som utnyttjar strukturen i den matematiska modellen av problemet för att erhålla bra prestanda och lägre krav på beräkningskraft. Resultaten visar att den föreslagna metoden förbättrar träffsäkerheten på frekvensföljningen i vissa fall, vilket kan förbättra prestanda för hastighetsskattningen. Den andra tillämpningen handlar om att skatta ett fordons hastighet med enbart en accelerometer (mätare av acceleration) fastsatt i chassit. Hjulvibrationer kan mätas av denna accelerometer. Frekvenserna av dessa vibrationer ges av hjulaxelns rotationshastighet. Om hjulradien är känd eller skattad så kan man räkna ut fordonets hastighet, så att man inte behöver använda externa mätningar som gps eller hjulhastighetsmätningar. Accelerationsmätningarna är brusiga och innehåller outliers, vilka är mätvärden som ibland slumpmässigt kraftigt skiljer sig från det förväntade. Därför studeras metoder som är konstruerade för att hantera dessa. Det föreslås en approximation till Rao-Blackwellisering för att kunna hantera dessa outliers. Det föreslås också en ny frekvensskattningsmetod baserad på expectation-maximization, vilket är ytterligare en metod som utnyttjar strukturer i matematiska modeller. En simuleringsstudie visar att metoderna har lägre genomsnittligt skattningsfel än standardmetoder. På insamlad experimentell data visas att metoderna ofta fungerar, men att de behöver kompletteras med en ytterligare komponent för död räkning (prognosvärden) med accelerometer för att öka antalet testfall där de erhåller godtagbar prestanda.

The instant demand to achieve high data rate in communication systems is driving the high-speed links into multi Gigabit per second data transitions, where its suffering from inter symbol interference due to bandwidth limitation. Equalizers are used at both the transmitter and receiver side of the link to counteract signal attenuation, reflections, crosstalk and any type of distortion of the signal. 2-level pulse amplitude modulation is today the most commonly used signal modulator. To achieve higher data rates, but remaining the same bandwidth, higher order pulse amplitude modulation must be used. The disadvantage is that the signal-to-noise ratio gets worse, which increases the bit error rate. Forward error correction is a method to reduce the bit error rate over a noisy or unreliable channel. This master thesis is about investigating forward error correction as an equalization method, to compensate for the increased bit error rate when using higher order signal modulation. Reed Solomon forward error corrector was implemented, which has its strength in correcting burst of errors. Two different testbenches were used to create the same errors that appears in a real channel. Probability plots were used to investigate how the Reed Solomon could compensate at low bit error rate regions. The probability plots showed that the Reed Solomon (544,514) would be able to Reduce the bit error rate from down to . The same Reed Solomon was used in the channel simulations, where the output bit error rate was correlating to the probability plots.

In this thesis a terrain positioning method for underwater vehicles called the correlation method is presented. Using the method the vehicle can determine its absolute position with the help of a sonar and a map of the bottom topography. The thesis is focused towards underwater positioning but most of the material is directly applicable to flying vehicles as well. The positioning of surface vehicles has been revolutionized by the global positioning system (GPS). However, since the GPS signal does not penetrate into the sea water volume, underwater vehicles still have to use the inertial navigation system (INS) for navigation. Terrain positioning is therefore a serious alternative to GPS for underwater vehicles for zeroing out the INS error in military applications. The thesis begins with a review of different estimation methods as Bayesian and extended Kalman filter methods that have been used for terrain navigation. Some other methods that may be used as the unscented Kalman filter or solving the Fokker-Planck equation using finite element methods are also discussed. The correlation method is then described and the well known problem with multiple terrain positions is discussed. It is shown that the risk of false positions decreases exponentially with the number of measurement beams. A simple hypothesis test of false peaks is presented. It is also shown that the likelihood function for the position under weak assumptions converges to a Gaussian probability density function when the number of measuring beams tends to infinity. The Cramér-Rao lower bound on the position error covariance is determined and it is shown that the proposed method achieves this bound asymptotically. The problem with measurement bias causing position bias is discussed and a simple method for removing the measurement bias is presented. By adjusting the footprint of the measuring sonar beams to the bottom topography a large increase in accuracy and robustness can be achieved in many bottom areas. This matter is discussed and a systematic theory about how to choose way-points is developed. Three sea-trials have been conducted to verify the characteristics of the method and some results from the last one in October 2002 are presented. The sea-trials verify to a very high degree the theory presented. Finally the method is briefly discussed under the assumption that the bottom topography can be described by an autoregressive stochastic process. / <p>QC 20101014</p>

The overall purpose with this thesis is to investigate and provide computationally efficient methods for estimation and detection. The focus is on airborne applications, and we seek estimation and detection methods which are accurate and reliable yet effective with respect to computational load. In particular, the methods shall be optimized for terrain-aided navigation andcollision avoidance respectively. The estimation part focuses on particle filtering and the in general much more efficient marginalized particle filter. The detection part focuses on finding efficient methods for evaluating the probability of extreme values. This is achieved by considering the, in general, much easier task to compute the probability of level-crossings. The concept of aircraft navigation using terrain height information is attractive because of the independence of external information sources. Typicallyterrain-aided navigation consists of an inertial navigation unit supported by position estimates from a terrain-aided positioning (TAP) system. TAP integrated with an inertial navigation system is challenging due to its highly nonlinear nature. Today, the particle filter is an accepted method for estimation of more or less nonlinear systems. At least when the requirements on computational load are not rigorous. In many on-line processing applications the requirements are such that they prevent the use of theparticle filter. We need more efficient estimation methods to overcome this issue, and the marginalized particle filter constitutes a possible solution. The basic principle for the marginalized particle filter is to utilize linear and discrete substructures within the overall nonlinear system. These substructures are used for efficient estimation by applying optimal filters such as the Kalman filter. The computationally demanding particle filter can then be concentrated on a smaller part of the estimation problem. The concept of an aircraft collision avoidance system is to assist or ultimately replace the pilot in order to to minimize the resulting collision risk. Detection is needed in aircraft collision avoidance because of the stochastic nature of thesensor readings, here we use information from video cameras. Conflict is declared if the minimum distance between two aircraft is less than a level. The level is given by the radius of a safety sphere surrounding the aircraft.We use the fact that the probability of conflict, for the process studied here, is identical to the probability for a down-crossing of the surface of the sphere. In general, it is easier to compute the probability of down-crossings compared to extremes. The Monte Carlo method provides a way forward to compute the probability of conflict. However, to provide a computationally tractable solution we approximate the crossing of the safety sphere with the crossing of a circular disc. The approximate method yields a result which is as accurate as the Monte Carlo method but the computational load is decreased significantly.

In this thesis we present the concept of concatenated multilevel codes. These codes are a combination of generalized concatenated codes with multilevel coding. The structure of these codes is simple and relies on the concatenation of two or more codes of shorter length. These codes can be designed to have large diversity which makes them attractive for use in fading channels. We also present an iterative decoding algorithm taylored to fit the properties of the proposed codes. The iterative decoding algorithm we present has a complexity comparable to the complexity of GMD decoding of the same codes. However, The gain obtained by using the iterative decoder as compared to GMD decdoing of these codes is quite high for Rayleigh fading channels at bit error rates of interest. Some bounds on the performance of these codes are given in this thesis. Some of the bounds are information theoretic bounds which can be used regardless of the code under study. Other bounds are on the error probability of concatenated multilevel codes. Finally we give examples on the implementation of these codes in adaptive coding of OFDM channels and MIMO channels. / QC 20100629

This thesis deals with the problem of classifying automotive vehicle occupants and estimating their position. This information is critical in designing future smart airbag systems providing maximum protection for passengers. According to the American National Highway Traffic Safety Administration (NHTSA), since 1990, in the USA, 227 deaths have been attributed to airbags deployed in low-speed crashes which included 119 children, and 22 infants. In these cases, intelligent deployment of the airbag, based on the type and position of occupant could have avoided these fatalities. Current commercial classification systems based on traditional sensors, such as pressure sensors are not able to detect the position of occupants. Vision-based systems are advantageous over pressure sensor based systems, as they can provide additional functionalities like dynamic occupant position analysis or child seat orientation detection. On the other hand, vision-based systems have to cope with several challenges, such as, illumination conditions, temperature, humidity, large variation of scenes, cost, and computational aspects. This thesis presents new pattern recognition techniques for classifying, localizing and tracking vehicle occupants using a low-resolution 3-D optical time-of-flight range camera. This sensor is capable of providing directly a dense range image, independent of the illumination conditions and object textures. Based on this technology, IEE S.A. is presently developing a camera system for the application of occupant classification. A prototype of this camera has been the basis for this study. The first part of the thesis presents the problem of occupant classification. Herein, we investigate geometric feature extraction methods to discriminate between different occupant types. We develop features that are invariant under rotation and translation. A method for reducing the size of the feature set is analyzed with emphasis on robustness and low computational complexity while maintaining highly discriminative information. In addition, several classification methods are studied including Bayes quadratic classifier, Gaussian Mixture Model (GMM) classifier and polynomial classifier. We propose the use of a cluster based linear regression classifier using a polynomial kernel which is particularly well suited to coping with large variations within each class. Full scale experiments have been conducted which demonstrate that a classification reliability of almost 100\% can be achieved with the reduced feature set in combination with a cluster based classifier. In this safety critical application, it is equally important to address the problem of reliability estimation for the system. State-of-the-art methods to estimate the reliability of the classification are based either on classification output or based on density estimation. The second part of the thesis treats estimation of the reliability of the pattern classification system. Herein, a novel reliability measure is proposed for classification output which takes into account the local density of training data. Experiments verify that this reliability measure outperforms state-of-the-art methods in many cases. Lastly, the problem of dynamically detecting out-of-position occupants is addressed in the third part of the thesis. This task requires detecting and localizing the position of the occupant's head. Traditional head detection methods, such as detecting head-like objects in the image by analyzing the local shapes are not robust with the current sensor. Many regions in a scene such as the shoulder or the elbow of the occupant can be incorrectly detected as the head. In order to cope with these challenges, we exploit topology information in the range image. A modified Reeb graph technique has been developed that extracts a topological skeleton of the 3D contour that is invariant under rotation and translations. Results verify that the Reeb graph detects successfully the head i.e., the head always corresponds to one of the end points of the skeleton. Subsequently, a data association algorithm to select the correct head candidate out of the Reeb graph candidates is presented. Results show that the resulting head detection algorithm based on Reeb graphs is robust under scene changes. / QC 20100714

Within this thesis, the satellite broadcast scenario of geostationary satellites is reviewed. The densely crowded geostationary arc in the common broadcast frequencies may create significant interference from adjacent satellites (ASI). The possible use of multiple-input receivers and of interference processing techniques is analyzed in this specific context. In addition the synchronization problem is studied under interference limited conditions for broadcast as well as broadband satellite systems.We address fixed satellite broadcast reception with the goal of decreasing the aperture of the receiving antenna. The front-end antenna size is commonly defined by the presence of interference from adjacent satellites. A small antenna aperture leads to interference from neighboring satellites utilizing the same frequency bands. We propose a multi-input reception system with subsequent joint detection which provides reliable communication in the presence of multiple interfering signals. An iterative least square technique is adopted combining spatial and temporal processing. This approach achieves robustness against pointing errors and against changing interference scenarios. Different temporal interference processing methods are evaluated, including Minimum Mean Square Error (MMSE) based iterative soft-decision interference cancellation as well as Iterative Least Square with Projection (ILSP) based approaches, which include spatial and temporal iterations. Furthermore the potential of an additional convolutional channel decoding step in the interference cancellation mechanism is verified.Also, we demonstrate how to accurately synchronize the signals as part of the detection procedure. The technique is evaluated in a realistic simulation study representing the conditions encountered in typical broadcast scenarios.In a second part of the thesis the problem of synchronization is reviewed in the context of interference limited scenarios for broadband satellite return channels. Spectral efficiency is of great concern in the return channel of satellite based broadband systems. In recent work the feasibility of increased efficiency by reducing channel spacing below the Symbol Rate was demonstrated using joint detection and decoding for a synchronized system. We extend this work by addressing the critical synchronization problem in the presence of adjacent channel interference (ACI) which limits performance as carrier spacing is reduced.A pilot sequence aided joint synchronization scheme for a multi-frequency time division multiple access (MF-TDMA) system is proposed. Based on a maximum likelihood (ML) criterion, the channel parameters, including frequency, time and phase are jointly estimated for the channel of interest and the adjacent channels. The impact of ACI on the synchronization and detection performance is investigated. It is shown that joint channel parameter estimation outperforms single carrier synchronization with reasonable additional computational complexity in the receiver. Based on the proposed synchronization scheme in conjunction with an appropriate joint detection mechanism the carrier spacing can be reduced significantly compared to current systems providing a substantial increase in spectral efficiency / QC 20100727

This thesis deals with three estimation problems motivated by spatial signal processing using arrays of sensors. All three problems are approached using tools from estimation theory, including asymptotical analysis of performance and Cramér-Rao lower bound; Monte Carlo methods are used to evaluate small sample performance. The first part of this thesis treats direction of arrival estimation for narrowband signals. Most algorithms require the noise covariance matrix to be known or to possess a known structure. In many cases, the noise covariance is estimated from a separate batch of signal-free samples; in a non-stationary environment this sample set can be small. By deriving the Cramér-Rao bound in a form that can be compared to well-known results, we investigate the combined effects of finite sample sizes, both in the estimated noise covariance matrix and in the data with signals present. Under the same data model, we derive the asymptotical covariance of weighted subspace fitting, where the signal-free samples are used for whitening. The obtained expression suggests optimal weights that improve performance compared to the standard choice and that result in an asymptotically efficient estimate. In addition, we propose a new, asymptotically efficient, method based on the likelihood function. If the array is uniform and linear, then an iterative search can be avoided. We propose two such algorithms, based on the two general, iterative, algorithms discussed. We also treat the detection problem, and provide results that are useful in a joint detection and estimation algorithm based on the proposed estimators. Parameter estimation for the reduced rank linear regression is the second estimation problem treated in the thesis. It appears in, for example, system identification and signal processing for communications. We propose a new method based on instrumental variable principles and we analyze its asymptotical performance. The new method is asymptotically efficient if the noise is temporally white, and outperforms previously suggested algorithms when the noise is temporally correlated. As part of the estimation algorithm, the closest low rank approximation of a matrix, as measured under a weighted norm, has to be calculated. This problem lacks solution in the general case. We propose two new methods that can be computed in fixed time; both methods are approximate but asymptotically optimal as part of the estimation procedure in question. We also propose a new algorithm for the related rank detection problem. The third problem is that of estimating the covariance matrix of a multivariate stochastic process. In some applications, the structure of the problem suggests that the underlying, true, covariance matrix is the Kronecker product of two matrix factors. The covariance matrix of the channel realizations in multiple input multiple output (MIMO) communications systems can, under certain assumptions, have such Kronecker product structure. Moreover, the factor matrices can sometimes, in turn, be assumed to possess additional structure. We propose two asymptotically efficient estimators for the case where the channel realizations can be assumed known. Both estimators can be computed in fixed time; they differ in their small sample performance and in their ability to incorporate extra structure in the Kronecker factors. In a practical MIMO system, the channel realizations have to be estimated from training data. If the amount of training data is limited, then it is better to treat the training data, rather than the channel estimates, as inputs to the channel covariance estimator. We derive and analyze an estimator based on this new data model. This estimate can be computed in fixed time and the estimator is also able to optimally use extra structure in the factor matrices / QC 20100820

In this thesis a sampling time domain measurement system primarily intended for measurements on radio frequency power amplifiers is discussed. The need for such a measurement system is established. Impairments due to non-ideal measurement instruments are discussed as well as methods to compensate for these impairments. Techniques to improve upon the raw measurement performance of the measurement instruments with regard to bandwidth, dynamic range, linear and nonlinear distortion are discussed. | A method to simultaneously find the phase and amplitude ripple of a vector signal generator and a vector signal analyzer is presented. The method is verified with extensive measurements. Two techniques, frequency stitching and Zhu’s generalized sampling theorem, to extend the effective measurement bandwidth of the measurement system is discussed and evaluated with measurements. They are both found to be able to extend the effective bandwidth for measurements of output signals of nonlinear power amplifiers with more than five times. The measurement system is used for sampled input – output measurements of power amplifiers and the obtained data are fitted to different behavioral power amplifier models including memory. Some different behavioral models are evaluated and compared for different kinds of power amplifiers. A neural network model and extensions to the well-known parallel Hammerstein model are specifically discussed. The parallel Hammerstein model are also used together with frequency stitching and Zhu’s generalized sampling theorem. A general hardware and software structure of a versatile measurement system based on virtual instruments for measurements on power amplifiers is discussed in some detail. Special attention is given to the software architecture and to the concepts of hardware and software reusability. An automated, fast, accurate and production-friendly method for two-tone power and frequency sweep measurements, including measurement of the phase of the intermodulation products in addition to the amplitude, is also presented. / QC 20100823