<p>To counter radio signal reconnaissance, an efficient way of covert communication is to use subsecond duration burst transmissions in the congested HF band. Against this background, the present thesis treats fast direction finding (DF) using antenna arrays with known response only in a few calibration directions. In such scenarios the known method of array mapping (interpolation) may be used to transform the output data vectors from the existing array onto the corresponding output vectors of another (virtual) array that is mathematically defined and optimally chosen. But in signal reconnaissance the emitters are initially unknown and the mapping matrix must be designed as a compromise over a wide sector of DOAs. This compromise may result in large DOA estimate errors, both deterministic and random. Analyzing, analytically describing, and minimizing these DOA errors, is the main theme of the present thesis. The first part of the thesis analyzes the deterministic mapping errors, the DOA estimate bias, that is caused by dissimilarity between the two array geometries. It is shown that in a typical signal reconnaissance application DOA estimate bias can dominate over DOA estimate variance. Using a Taylor series expansion of the DOA estimator cost function an analytical expression for the bias is derived and a first order zero bias condition is identified. This condition is general, estimator independent, and can be applied to any type of data pre-processing. A design algorithm for the mapping matrix is thereafter presented that notably reduces mapped DOA estimate bias. A special version is also given with the additional property of reducing the higher order Taylor terms and thus the residual bias. Simulations demonstrate a bias reduction factor exceeding 100 in some scenarios. A version based on signal subspace mapping rather than array manifold mapping is also given. This version is of large practical interest since the mapping matrix can be designed directly from calibration data. In the second part of the thesis the derived bias minimization theory is extended into Mean Square Error (MSE) minimization, i.e. measurement noise is introduced. Expressions for DOA error variance and DOA MSE under general pre-processing are derived, and a design algorithm for the mapping matrix is formulated by which mapped DOA estimate MSE can be minimized. Simulations demonstrate improved robustness and performance for this algorithm, especially in low SNR scenarios. In the third and final part of the thesis the theoretical results are supported by experimental data. For an 8 element circular array mapped onto a virtual ULA across a 600 sector it is shown that the mapped DOA estimate errors can be suppressed down to the Cramér-Rao level.</p>

<p>Path planning has important applications in many areas, for example industrial robotics, autonomous systems, virtual prototyping, and computer-aided drug design. This thesis presents a new framework for developing and evaluating path planning algorithms. The framework is named CoPP (Components for Path Planning). It consists of loosely coupled and reusable components that are useful for building path planning applications. The framework is especially designed to make it easy to do fair comparisons between different path planning algorithms. </p><p>CoPP is also designed to allow almost any user-defined moving system. The default type of moving system is a robot class, which is capable of describing tree-like kinematic chains. Additional features of this robot class are: joint couplings, numerical or closed-form inverse kinematics, and hierarchical robot representations. The last feature is useful when planning for complex systems like a mobile platform equipped with an arm and a hand. </p><p>During the last six years, Rapidly-exploring Random Trees (RRTs) have become a popular framework for developing randomized path planning algorithms. This thesis presents a method for augmenting bidirectional RRT-planners with local trees. For problems where the solution trajectory has to pass through several narrow passages, local trees help to reduce the required planning time. </p><p>To reduce the work needed for programming of industrial robots, it is desirable to allow task specifications at a very high level, leaving it up to the robot system to figure out what to do. Here we present a fast and flexible pick-and-place planner. Given an object that has to be moved to another position, the planner chooses a suitable grasp of the object and finds motions that bring the object to the desired position. The planner can also handle constraints on, e.g., the orientation of the manipulated object. </p><p>For planning of pick-and-place tasks it is necessary to choose a grasp suitable to the task. Unless the grasp is given, some sort of grasp planning has to be performed. This thesis presents a fast grasp planner for a three- fingered robot hand. The grasp planner could be used in an industrial setting, where a robot is to pick up irregularly shaped objects from a conveyor belt. In conjunction with grasp planning, a new method for evaluating grasp stability is presented.</p>

Path planning has important applications in many areas, for example industrial robotics, autonomous systems, virtual prototyping, and computer-aided drug design. This thesis presents a new framework for developing and evaluating path planning algorithms. The framework is named CoPP (Components for Path Planning). It consists of loosely coupled and reusable components that are useful for building path planning applications. The framework is especially designed to make it easy to do fair comparisons between different path planning algorithms. CoPP is also designed to allow almost any user-defined moving system. The default type of moving system is a robot class, which is capable of describing tree-like kinematic chains. Additional features of this robot class are: joint couplings, numerical or closed-form inverse kinematics, and hierarchical robot representations. The last feature is useful when planning for complex systems like a mobile platform equipped with an arm and a hand. During the last six years, Rapidly-exploring Random Trees (RRTs) have become a popular framework for developing randomized path planning algorithms. This thesis presents a method for augmenting bidirectional RRT-planners with local trees. For problems where the solution trajectory has to pass through several narrow passages, local trees help to reduce the required planning time. To reduce the work needed for programming of industrial robots, it is desirable to allow task specifications at a very high level, leaving it up to the robot system to figure out what to do. Here we present a fast and flexible pick-and-place planner. Given an object that has to be moved to another position, the planner chooses a suitable grasp of the object and finds motions that bring the object to the desired position. The planner can also handle constraints on, e.g., the orientation of the manipulated object. For planning of pick-and-place tasks it is necessary to choose a grasp suitable to the task. Unless the grasp is given, some sort of grasp planning has to be performed. This thesis presents a fast grasp planner for a three- fingered robot hand. The grasp planner could be used in an industrial setting, where a robot is to pick up irregularly shaped objects from a conveyor belt. In conjunction with grasp planning, a new method for evaluating grasp stability is presented.

To counter radio signal reconnaissance, an efficient way of covert communication is to use subsecond duration burst transmissions in the congested HF band. Against this background, the present thesis treats fast direction finding (DF) using antenna arrays with known response only in a few calibration directions. In such scenarios the known method of array mapping (interpolation) may be used to transform the output data vectors from the existing array onto the corresponding output vectors of another (virtual) array that is mathematically defined and optimally chosen. But in signal reconnaissance the emitters are initially unknown and the mapping matrix must be designed as a compromise over a wide sector of DOAs. This compromise may result in large DOA estimate errors, both deterministic and random. Analyzing, analytically describing, and minimizing these DOA errors, is the main theme of the present thesis. The first part of the thesis analyzes the deterministic mapping errors, the DOA estimate bias, that is caused by dissimilarity between the two array geometries. It is shown that in a typical signal reconnaissance application DOA estimate bias can dominate over DOA estimate variance. Using a Taylor series expansion of the DOA estimator cost function an analytical expression for the bias is derived and a first order zero bias condition is identified. This condition is general, estimator independent, and can be applied to any type of data pre-processing. A design algorithm for the mapping matrix is thereafter presented that notably reduces mapped DOA estimate bias. A special version is also given with the additional property of reducing the higher order Taylor terms and thus the residual bias. Simulations demonstrate a bias reduction factor exceeding 100 in some scenarios. A version based on signal subspace mapping rather than array manifold mapping is also given. This version is of large practical interest since the mapping matrix can be designed directly from calibration data. In the second part of the thesis the derived bias minimization theory is extended into Mean Square Error (MSE) minimization, i.e. measurement noise is introduced. Expressions for DOA error variance and DOA MSE under general pre-processing are derived, and a design algorithm for the mapping matrix is formulated by which mapped DOA estimate MSE can be minimized. Simulations demonstrate improved robustness and performance for this algorithm, especially in low SNR scenarios. In the third and final part of the thesis the theoretical results are supported by experimental data. For an 8 element circular array mapped onto a virtual ULA across a 600 sector it is shown that the mapped DOA estimate errors can be suppressed down to the Cramér-Rao level. / QC 20101022

Frequency estimation has been studied for a large number of years. One reason for this is that the problem is easy to understand, but difficult to solve. Another reason, for sure, is the large number of applications that involve frequency estimation, e.g radar using frequency modulated continuous wave (FMCW) techniques where the distance to the target is embedded in the frequency, resonance sensor systems where the output signal is given as the frequency displacement from a nominal frequency, radio frequency identification systems (RFID) where frequency modulation is used in the communication link, etc. The requirement on the frequency estimator varies with the application and typical issues include: accuracy, precision or (bias) processing speed or complexity, and ability to handle multiple signals. A lot of solutions to different problems in this area has been proposed, but still several open questions remain. The first part of this thesis addresses the problem of frequency estimation using low complexity algorithms. One way of achieving such an algorithm is to employ a coarse quantization on the input signal. In this thesis, a 1-bit quantizer is considered which enables the use of low complexity algorithms. Frequency estimation using look-up tables is studied and the properties of such an estimator are presented. By analyzing the look-up tables using the Hadamard transform a novel type of lowcomplexity frequency estimators is proposed. They use operations such as binary multiplication and addition of precalculated constants. This fact makes them suitable in applications where low complexity and high speed are major issues. A hardware demonstrator using the table look-up technique is designed and a prototype is analysed by real measurements. Today, the interest of using digital signal processing instead of analog processing is almost absolute. For example, in testing analog-to-digital converters an important part is to fit a sinewave to the recorded data, as well as to calculate the parameters that in least-squares sense result in the best fit. In this thesis, the sinewave fitting method included in the IEEE Standard 1057 is studied in some detail. Asymptotic Cramér-Rao bounds for three- and four model parameters are derived under the Gaussian assumption. Further, the sinewave fitting properties of the algorithm are analyzed by the parsimony principle. A novel model order selection criterion is proposed for waveform fitting methods in the case of a linear signal model. A generalization of this criterion is made to include the non-linear sinewave fitting application. For multiple sinewave fitting applications two iterative algorithms are proposed. The first method is a combination of the standardized sinewave fit algorithm and the expectation maximization algorithm. The second algorithm is an extension of a single sinewave model to a multiple sinewave model employing the standardized sinewave fitting algorithm. Both algorithms are analysed by numerical means and are shown to accurately resolve multiple sinewaves and produce efficient estimates. Initialization issues of such algorithms are included to some extent. / QC 20100830

Since its discovery, in the 1940's, radar (Radio Detection and Ranging) has become an important ranging sensor in many areas of technology and science. Most of the military and many civilian applications are unimaginable today without radar. With technology development, radar application areas have become larger and more available. One of these applications is Synthetic Aperture Radar (SAR), where an airborne radar is used to create high resolution images of the imaged scene. Although known since the 1950's, the SAR methods have been continuously developed and improved and new algorithms enabling real-time applications have emerged lately. Together with making the hardware components smaller and lighter, SAR has become an interesting sensor to be mounted on smaller unmanned aerial vehicles (UAV's). One important thing needed in the SAR algorithms is the estimate of the platform's motion, like position and velocity. Since this estimate is always corrupted with errors, particularly if lower grade navigation system, common in UAV applications, is used, the SAR images will be distorted. One of the most frequently appearing distortions caused by the unknown platform's motion is the image defocus. The process of correcting the image focus is called auto-focusing in SAR terminology. Traditionally, this problem was solved by methods that discard the platform's motion information, mostly due to the off-line processing approach, i.e. the images were created after the flight. Since the image (de)focus and the motion of the platform are related to each other, it is possible to utilise the information from the SAR images as a sensor and improve the estimate of the platform's motion. The auto-focusing problem can be cast as a sensor fusion problem. Sensor fusion is the process of fusing information from different sensors, in order to obtain best possible estimate of the states. Here, the information from sensors measuring platform's motion, mainly accelerometers, will be fused together with the information from the SAR images to estimate the motion of the flying platform. Two different methods based on this approach are tested on the simulated SAR data and the results are evaluated. One method is based on an optimisation based formulation of the sensor fusion problem, leading to batch processing, while the other method is based on the sequential processing of the radar data, leading to a filtering approach. The obtained results are promising for both methods and the obtained performance is comparable with the performance of a high precision navigation aid, such as Global Positioning System (GPS). / LINK-SIC

Today, wireless communications are an essential part of our everyday life. Both the number of users and their demands for wireless data have increasedtremendously during the last decade. Multiantenna communicationsis a promising solution to meet this ever-growing traffic demands. However, impairments that exist in most practical communication networks may substantially limit the performance of a multiantenna system. The characterizationof such a performance loss and how to minimize that are still largelyopen problems. The present thesis addresses this important research gap. Inparticular, we focus on three major impairments of a multiantenna cellularnetwork: impairment in the channel state information (CSI), interference andimpairment in the transceiver hardware components.To fully realize the benefits of multiantenna communications, the users need to acquire a certain level of information about their propagation environment; that is, their corresponding CSI. In practice, the CSI is not known bythe users and should be acquired by allocating part of the network resourcesfor pilot transmission. This problem is mainly important in the systems with a large number of antennas, as in general the required network resources for CSI acquisition scales with the number of transmitting antennas. Theproblem of CSI acquisition in a single-cell multiuser multiple-input multipleoutput(MIMO) system is addressed in this thesis. A linear spatial precodingand combining scheme for pilot transmission is proposed. This scheme requiresless number of network resources for channel estimation compared tothe conventional schemes. The gains of the proposed scheme are characterized by finding an upper-bound and a lower-bound on the channel estimation error.Moreover, as an ultimate performance metric, an achievable sum-rate ofthe network is formulated and analyzed numerically.Due to the broadcast nature of the wireless channels, the performanceof the users in a network is intertwined; the desired signal of one user mayinterfere other users. Hence, the interference is another major impairment inwireless communication systems. In this thesis, the practical challenges of aninterference management technique, namely MIMO interference alignment isinvestigated by implementation on a multiuser MIMO testbed. Then, in thecontext of interference alignment, the problem of optimal power allocation forpilot and data transmission is studied and verified by the measurements.The impairment in the hardware components of the transceivers, that is, any deviation of the components from their ideal behavior, degrades the performance of a communication system. In particular, the impact of nonlineartransmitter power amplifiers (PA)s is investigated in this thesis. First, consideringa memoryless third-order polynomial model for the PAs, a model forthe transmitted nonlinear distortion signal from a multiantenna transmitter isproposed and validated by measurements. This model implies that the spatialdirection of the transmitted distortion is dependent on the spatial directionof the desired signal. Then, this model is extended for a general arbitrary order polynomial model. Exploiting the developed distortion model, the energyefficiency of a multiantenna system operating at millimeter wave frequenciesis studied. / <p>QC 20170523</p>

The use of ultrasound to measure preload in screws and bolts has been studied quite frequently the last decades. The technique is based on establishing a relationship between preload and change in time of flight (TOF) for an ultrasonic pulse propagating back and forth through a screw. This technique has huge advantages compared to other methods such as torque and angle tightening, mainly because of its independence of friction. This is of great interest for Atlas Copco since it increases the accuracy and precision of their assembly tools. The purpose of this thesis was to investigate ultrasonic wave propagation in pre-stressed screws using a simulation software, ANSYS, and to analyse the results using signal processing. The simulations were conducted in order to get an understanding about the wavefront distortion effects that arise. Further, an impulse response of the system was estimated with the purpose of dividing the multiple echoes that occur from secondary propagation paths from one other. The results strengthen the hypothesis that the received echoes are superpositions of reflections taking different propagation paths through the screw. An analytical estimation of the wavefront curvature also shows that the wavefront distortion due to a higher stress near the screw boundaries can be neglected. Additionally, a compressed sensing technique has been used to estimate the impulse response of the screw. The estimated impulse response models the echoes as superpositions of secondary echoes, with significant taps corresponding to the TOF of the shortest path and a mode-converted echo. The method is also shown to be stable in noisy environments. The simulation model gives rise to a slower speed of sound than expected, which most likely is due to the fact that finite element analysis in general overestimates the stiffness of the model.

In underwater acoustic communications the time variation in the channel is a huge chal- lenge. The estimation of the impulse response at the receiver is crucial for the decoding of the signal to become accurate. One way is to transmit a superimposed pilot sequence along the unknown message, and by the knowledge of the sequence have the possibility to continuously track the variation in the channel over time. This thesis investigates if it is possible by the aid of superimposed pilot sequences to separate the taps in the channel impulse response and using a parametric method to describe the taps as polynomial phase signals. The method used for separation of the taps was a moving least squares estimator. Thereafter each tap was optimised to a polynomial phase signal (PPS) using a weighted non-linear least squares estimator. The non-linear parameters of the model was then determined with the Levenberg-Marquardt method. The performance of the method was evaluated both for simulated data as well as for data from eld tests. The performance was determined by calculating the mean squared error (MSE) of the model over dierent frame lengths, signal to noise ratio (SNR), weights for the superimposed pilots, rapidness of time variation and impulse response lengths. The method was not sensitive to the properties of the channel. Even though the model had high performance, the complexity of the computations generated long compilation times. Hence, the method needs further work before a real time implementation could be possible.

This work is about digital signal processing methods to be used to determine information of low frequency low amplitude signals of known frequency. Different adaptive filter concepts such as Wiener filter, NLMS filter and lock-in are implemented and compared to each other. The comparison carried out for different input signal amplitude and noise variance with the objective to find the best algorithm for noise cancelling. The comparison is done using a signal of interest combined with white noise as input to the filter element. The aim of the comparison is to find the most appropriate filter for further signal analyzis. The key topics for the evaluation are the efficiency of noise cancelling and ease of implementation in a data processing unit.