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

Spatial reuse TDMA has been proposed as an access scheme for multi-hop radio networks where real-time service guarantees are important. The idea is to allow several radio terminals to use the same time slot when possible. A time slot can be shared when the radio units are geographically separated such that small interference is obtained. The transmission rights of the different users are described with a schedule. In this thesis we will study various aspects of STDMA scheduling. A common thread in these various aspects is the use of an interference-based network model, as opposed to a traditional graph-based network model. While an interference-based network model is more complex than a graph-based model, it is also much more realistic in describing the wireless medium. An important contribution of this thesis is a comparison of network models where we show that the limited information of a graph model leads to significant loss of throughput as compared to an interference-based model, when performing STDMA scheduling. The first part ot this thesis is a study of assignment strategies for centralized scheduling. Traditionally, transmission rights have been given to nodes or to links, i.e., transmitter/receiver pairs. We compare these two approaches and show that both have undesirable properties in certain cases. Furthermore, we propose a novel assignment strategy, achieving the advantages of both methods. Next we investigate the effect of a limited frame length on STDMA schedules. We first show that the required frame length is larger for link assignment than for node assignment. Further, we propose a novel assignment strategy, the joint node and link assignment, that has as low frame length requirements as node assignment but with the capacity of link assignment. In the last part of this thesis we describe a novel interfence-based distributed STDMA algorithm and investigate its properties, specifically its overhead requirement. In addition we show that this algorithm can generate as good schedules as a centralized algorithm can. / QC 20101015

Spectrum regulation is tricky and until recently the methods used for almost a century has sufficed. But as wireless communication has increased the demands on spectrum has increased. The regulators have responded by relaxing the current regulatory framework as well as opening up more bands for license exempt or unlicensed operation. In unlicensed spectrum users can be expected to act greedily and possibly also break etiquette rules. Using game theory we find that in most cases a user benefits form acting greedily and this decrease total system capacity. It is possible to deter a user from cheating by applying punishment to the user. This function should preferably be incorporated in the access network. We also study the case of networks competing in unlicensed spectrum and find that the most successful network is the one with lowest quality guarantees and with the most dense access network. In the case studied here the greedy behavior of the networks increases the spectrum utilization. We also evaluate a number of cases where two networks that cooperate in unlicensed spectrum. Isolation between the networks is the key factor to achieve better performance than splitting the spectrum. The evaluations are carried out using numerical experiments and game theory. Game theory ia a powerful tool for modelling coexistence problems in unlicensed spectrum, but the systems are too complex to allow a fully analytical treatment. / QC 20101012

Today many types of communication are employed on seagoing vessels, such as radio, satellite and Wi-Fi but only one type of communication is practical for submerged vessels, the acoustic underwater modem. The "off-the-shelf" modems are sometimes difficult to update and replace, especially on a large submarine. But by separating the hardware from the signal processing and making the software modular more versatility can be achieved.   The questions that this thesis are asking are: is it possible to implement the signal processing in software? How small or large should the modules be? What kind of architecture should be used? This thesis shows that it is indeed possible to implement simple algorithms that can isolate a signal and read its content regardless of the hardware configuration. Calculations show that up to 13 kbps can be reached at a range of one kilometer. It is most practical to make the entire physical layer into one module and the size of the system could drastically change the type of architecture used.

This masters thesis describes the development of signal processing and patternrecognition in monitoring Parkison’s disease. It involves the development of a signalprocess algorithm and passing it into a pattern recogniton algorithm also. Thesealgorithms are used to determine , predict and make a conclusion on the study ofparkison’s disease. We get to understand the nature of how the parkinson’s disease isin humans.

Biological phenomena in the cells can be explained in terms of the interactions among biological macro-molecules, e.g., DNAs, RNAs and proteins. These interactions can be modeled by genetic regulatory networks (GRNs). This dissertation proposes to reverse engineering the GRNs based on heterogeneous biological data sets, including time-series and time-independent gene expressions, Chromatin ImmunoPrecipatation (ChIP) data, gene sequence and motifs and other possible sources of knowledge. The objective of this research is to propose novel computational methods to catch pace with the fast evolving biological databases. Signal processing techniques are exploited to develop computationally efficient, accurate and robust algorithms, which deal individually or collectively with various data sets. Methods of power spectral density estimation are discussed to identify genes participating in various biological processes. Information theoretic methods are applied for non-parametric inference. Bayesian methods are adopted to incorporate several sources with prior knowledge. This work aims to construct an inference system which takes into account different sources of information such that the absence of some components will not interfere with the rest of the system. It has been verified that the proposed algorithms achieve better inference accuracy and higher computational efficiency compared with other state-of-the-art schemes, e.g. REVEAL, ARACNE, Bayesian Networks and Relevance Networks, at presence of artificial time series and steady state microarray measurements. The proposed algorithms are especially appealing when the the sample size is small. Besides, they are able to integrate multiple heterogeneous data sources, e.g. ChIP and sequence data, so that a unified GRN can be inferred. The analysis of biological literature and in silico experiments on real data sets for fruit fly, yeast and human have corroborated part of the inferred GRN. The research has also produced a set of potential control targets for designing gene therapy strategies.

<p>In order to ensure secure communication in digital military radio systems, multiple methods are used to protect the transmission from being intercepted by enemy electronic warfare systems. An intercepted transmission can be used to estimate several parameters of the transmitted signal such as its origin (position or direction) and of course the transmitted message itself. The methods used in traditional electronic warfare direction-finding systems have in general poor performance against wideband low power signals while the considered correlation-based time-difference of arrival (TDOA) methods show promising results.</p><p>The output from a TDOA-based direction-finding system using two spatially separated receivers is the TDOA for the signal between the receiving sensors which uniquely describes a hyperbolic curve and the emitter is located somewhere along this curve. In order to measure a TDOA between two digital radio receivers both receiver systems must have the same time and frequency references to avoid degradation due to reference imperfections. However, in some cases, the receivers are separated up to 1000 km and can not share a common reference. This is solved by using a reference module at each of the receiver sites and high accuracy is achieved using the NAVSTAR-GPS system but, still, small differences between the outputs of the different reference modules occurs which degrades the performance of the system.</p><p>In a practical electronic warfare system there is a number of factors that degrade the performance of the system, such as non-ideal antennas, analog receiver filter differences, and the analog to digital converter errors. In this thesis we concentrate on the problems which arises from imperfections in the reference modules, such as time and frequency errors.</p>

<p>Spatial reuse TDMA has been proposed as an access scheme for multi-hop radio networks where real-time service guarantees are important. The idea is to allow several radio terminals to use the same time slot when possible. A time slot can be shared when the radio units are geographically separated such that small interference is obtained. The transmission rights of the different users are described with a schedule.</p><p>In this thesis we will study various aspects of STDMA scheduling. A common thread in these various aspects is the use of an interference-based network model, as opposed to a traditional graph-based network model. While an interference-based network model is more complex than a graph-based model, it is also much more realistic in describing the wireless medium. An important contribution of this thesis is a comparison of network models where we show that the limited information of a graph model leads to significant loss of throughput as compared to an interference-based model, when performing STDMA scheduling.</p><p>The first part ot this thesis is a study of assignment strategies for centralized scheduling. Traditionally, transmission rights have been given to nodes or to links, i.e., transmitter/receiver pairs. We compare these two approaches and show that both have undesirable properties in certain cases. Furthermore, we propose a novel assignment strategy, achieving the advantages of both methods.</p><p>Next we investigate the effect of a limited frame length on STDMA schedules. We first show that the required frame length is larger for link assignment than for node assignment. Further, we propose a novel assignment strategy, the joint node and link assignment, that has as low frame length requirements as node assignment but with the capacity of link assignment.</p><p>In the last part of this thesis we describe a novel interfence-based distributed STDMA algorithm and investigate its properties, specifically its overhead requirement. In addition we show that this algorithm can generate as good schedules as a centralized algorithm can.</p>

<p>This thesis begins with a survey of current state-of-the art in-car navigation systems. The pros and cons of the four commonly used information sources — GNSS/RF-based positioning, vehicle motion sensors, vehicle models and map information — are described. Common filters to combine the information from the various sources are discussed.</p><p>Next, a GNSS-aided inertial navigation platform is presented, into which further sensors such as a camera and wheel-speed encoder can be incorporated. The construction of the hardware platform, together with an extended Kalman filter for a closed-loop integration between the GNSS receiver and the inertial navigation system (INS), is described. Results from a field test are presented.</p><p>Thereafter, an approach is studied for calibrating a low-cost inertial measurement unit (IMU), requiring no mechanical platform for the accelerometer calibration and only a simple rotating table for the gyro calibration. The performance of the calibration algorithm is compared with the Cramér-Rao bound for cases where a mechanical platform is used to rotate the IMU into different precisely controlled orientations.</p><p>Finally, the effects of time synchronization errors in a GNSS-aided INS are studied in terms of the increased error covariance of the state vector. Expressions for evaluating the error covariance of the navigation state vector are derived. Two different cases are studied in some detail. The first considers a navigation system in which the timing error is not taken into account by the integration filter. This leads to a system with an increased error covariance and a bias in the estimated forward acceleration. In the second case, a parameterization of the timing error is included as part of the estimation problem in the data integration. The estimated timing error is fed back to control an adjustable fractional delay filter, synchronizing the IMU and GNSS-receiver data.</p>