During environmental research projects in the Arctic region AFRY has come across an unproportionally high number of cases where the navigation of drones have not worked as intended, compared to other regions. The main objective of this thesis is to investigate the cause of these navigational problems and determine the main cause. A second objective is to design a solution that can mitigate these observed errors and improve the navigation. To establish the main error sources flight logs from flight tests performed at Svalbard are analyzed. The drone considered in this project is a quadcopter with a Pixhawk Cube flight controller and the Ardupilot software. A Pixhawk Here+ module is used for external sensors. The data logs show several cases of drones having troubles flying along a straight line. Analyzing the sensor data for the flights show that many of the flights suffer from the gyroscope drifting around the z-axis. The data show that the varying temperature on the IMU board is the cause of the drifting gyroscope. The on-board heaters did not manage to keep the temperature constant due to a too high target temperature and low outside temperatures. The system is aided with information from the magnetometer to estimate the drift around the z-axis. Results show that the estimation system is having trouble correctly estimating large drifts. To investigate why the magnetometer cannot properly compensate for the gyroscope, simulations of the magnetometer and estimation system are made. The results show that an increasing angle of inclination increases the gyroscope bias estimation errors. The large angle of inclination causes the horizontal components of the magnetic field to become too small for the magnetometer to measure correctly. The solution consists of instructions on how to operate the drone to properly use the on-board heaters, as well as an external module consisting of multiple magnetometers. Multiple magnetometers reduced the variance in the readings, but did not reach the accuracy needed to replace the external magnetometers on the drone. A better calibration method could be explored in the future, or another solution such as an improved magnetometer, a gyro compass, a GPS compass or dual GNSS antennas.

In this thesis capsule networks are investigated, both theoretically and empirically. The properties of the dynamic routing [42] algorithm proposed for capsule networks, as well as a routing algorithm in a follow-up paper by Wang et al. [50] are thoroughly investigated. It is conjectured that there are three key attributes that are needed for a good routing algorithm, and these attributes are then related to previous algorithms. A novel routing algorithm EntMin is proposed based on the observations from the investigation of previous algorithms. A thorough evaluation of the performance of different aspects of capsule networks is conducted, and it is shown that EntMin outperforms both dynamic routing and Wang routing. Finally, a capsule network using EntMin routing is compared to a very deep Convolutional Neural Network and it is shown that it achieves comparable performance.

The indoor localization of mobile users is currently a central issue for many applications and fields, including sensor networks, asset management, healthcare, ambient-assisted living, and public safety personnel localization. Existing solutions often rely on the fusion of information from multiple sensors. The potential of using an ultra wideband (UWB) system for wireless distance measurement based on the round-trip time (RTT) has been investigated in this thesis. Non-coherent UWB receivers have been analyzed using two different approaches: amplitude detection and energy detection. Both non-coherent UWB receivers front ends have been designed and implemented. Simulations of the measurement performance are also provided. Furthermore, a method has been proposed using undersampling over a burst of UWB pulses to reconstruct the original pulse and try to approximate the optimal performance of the ideal UWB receiver. The simulations yield interesting results regarding the performance of the RTT estimation. Both detection techniques are compared, describing the advantages and disadvantages of each one.

This report can be divided into two parts, the Integration of Inertial Navigation System (INS) and GPS and Adaptive beamforming for a GPS antenna array. GPS and INS have complemen-tary properties. Therefore integration results in enhanced performance and robustness compared to each of the two systems alone. In the first part a tightly coupled navigation filter, based on code pseudorange measurements and an IMU with Litton LN 200 performance has been derived. The filter is used in a com-parison of a stand-alone receiver and differential GPS (two receivers). The navigation filter is a complementary extended Kalman estimator, where combinations of GPS pseudoranges and pseudoranges predicted by the INS are used as observations. Systematic errors in the inertial sensors are estimated and compensated for, which gives an improved navigation performance also during periods without GPS aiding. Results are given as simulations where the different integration methods have been tested using partial satellite outages, different flight paths and atmospheric disturbances. The second part of the report shows how the GPS/INS based navigation system can be made even more robust by using an adaptively beamforming antenna. The received GPS signals are extremely weak and therefore vulnerable to interfering signals. The robustness and performance of the beamforming algorithm LCMV is tested in static simulation scenarios, with multiple jam-mers and different numbers of array elements. Also, navigation performance is tested for a flying vehicle using the tightly coupled navigation filter in a dynamic scenario with severe jamming from airborne jammers. Using the most effective algorithm, multiple outputs LCMV, increases the equivalent C/N0 with more than 40 dB, in the dynamic scenario.

The demands for high data rates and broadband wireless access require the development of wireless systems that can support wide and multi-band signals. To deploy these signals, new radio frequency (RF) front-ends are required which impose new challenges in terms of power consumption efficiency and sources of distortion e.g., nonlinearity. These challenges are more pronounced in power amplifiers (PAs) that degrade the overall performance of the RF transmitter. Since it is difficult to optimize the linearity and efficiency characteristics of a PA simultaneously, a trade-off is needed. At high input power, a PA exhibits high efficiency at the expense of linearity. On the other hand, at low input power, a PA is linear at the expense of the efficiency. To achieve linearity and efficiency at the same time, digital pre-distortion (DPD) is often used to compensate for the PA nonlinearity at high input power. In case of multi-channel PAs, input and output signals of different channels interact with each other due to cross-talk. Therefore, these PAs exhibit different nonlinear behavior than the single-input single-output (SISO) PAs. The DPD techniques developed for SISO PAs do not result in adequate performance when used for multi-channel PAs. Hence, an accurate behavioral modeling is essential for the development of DPD for multi-channel RF PAs. In this thesis, we propose three novel behavioral models and DPD schemes for nonlinear multiple-input multiple-output (MIMO) transmitters in presence of cross-talk. A study of the source of cross-talk in MIMO transmitters have been investigated to derive simple and powerful modeling schemes. These models are extensions of a SISO generalized memory polynomial model. A comparative study with a previously published MIMO model is also presented. The effect of coherent and partially non-coherent signal generationon DPD performance is also highlighted. It is shown experimentally that with partially non-coherent signal generation, the performance of the DPD degrades compared to coherent signal generation. In context of multi-channel RF transmitters, PA behavioral models and DPD schemes suffer from a large number of model parameters with the increase in nonlinear order and memory depth. This growth leads to high complexity model identification and implementation. We have designed a DPD scheme for MIMO PAs using a sparse estimation technique for reducing model complexity. This technique also increases the numerical stability when linear least square estimation model identification is used. A method to characterize the memory effects in a nonlinear concurrent dual-band PAs is also presented. Compared to the SISO PAs, concurrent dual-band PAs are not only affected by intermodulation distortions but also by cross-modulation distortions. The characterization of memory effects inconcurrent dual-band transmitter is performed by injecting a two-tone test signal in each input channel of the transmitter. Asymmetric energy surfaces are introduced for the intermodulation and cross-modulation products, which can be used to identify the power and frequency regions where the memory effects are dominant. / <p>QC 20141217</p>

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When photos and videos are increasingly used as evidence material, it is of importance to know if these materials can be used as evidence material or if the risk of them being forged is impending. This thesis investigates methods for detecting anomalous regions in images and videos using photo-response non-uniformity -- a fixed-pattern sensor noise that can be estimated from photos or videos. For photos, experiments were performed on a method that assumes other photos from the same camera are available. For videos, experiments were performed on a method further developed from the still image method, with other videos from the same camera being available. The last experiments were performed on videos when only the video that was about to be investigated was available. The experiments on the still image method were performed on images with three different kinds of forged regions: a forged region from somewhere else in the same photo, a forged region from a photo taken by another camera, and a forged region from the same sensor position in a photo taken by the same camera. The method should not be able to detect the third kind of forged region. Experiments performed on videos had a forged region in several adjacent frames in the video. The forged region was from another video, and it moved and changed shape between the frames. The methods mainly consist of a classification process and some post-processing. In the classification process, features were extracted from images/videos and used in a random forest classifier. The results are presented in precision, recall, F1 score and false positive rate. The quality of the still images was generally better than the videos, which also resulted in better results. For the cameras used in the experiments, it seemed easier to estimate a good PRNU pattern from photos and videos from older cameras. Probably due to sensor differences and extra processing in newer camera models. How the images and videos are compressed also affects the possibility to estimate a good PRNU pattern, because important information may then be lost.

The mining industry is currently facing a transition from manually operated vehicles to remote or semi-automated vehicles. The vision is fully autonomous vehicles being part of a larger fleet, with humans only setting high-level goals for the autonomous fleet to execute in an optimal way. An enabler for this vision is the presence of robust, reliable and highly accurate localization. This is a requirement for having areas in a mine with mixed autonomous vehicles, manually operated vehicles, and unprotected personnel. The robustness of the system is important from a safety as well as a productivity perspective. When every vehicle in the fleet is connected, an uncertain position of one vehicle can result in the whole fleet begin halted for safety reasons. Providing reliable positions is not trivial in underground mine environments, where access to global satellite based navigation systems is denied. Due to the harsh and dynamically changing environment, onboard positioning solutions are preferred over systems utilizing external infrastructure. The focus of this thesis is localization systems relying only on sensors mounted on the vehicle, e.g., odometers, inertial measurement units, and 2D LIDAR sensors. The localization methods are based on the Bayesian filtering framework and estimate the distribution of the position in the reference frame of a predefined map covering the operation area. This thesis presents research where the properties of 2D LIDAR data, and specifically characteristics when obtained in an underground mine, are considered to produce position estimates that are robust, reliable, and accurate. First, guidelines are provided for how to tune the design parameters associated with the unscented Kalman filter (UKF). The UKF is an algorithm designed for nonlinear dynamical systems, applicable to this particular positioning problem. There exists no general guidelines for how to choose the parameter values, and using the standard values suggested in the literature result in unreliable estimates in the considered application. Results show that a proper parameter setup substantially improves the performance of this algorithm. Next, strategies are developed to use only a subset of available measurements without losing quality in the position estimates. LIDAR sensors typically produce large amounts of data, and demanding real-time positioning information limits how much data the system can process. By analyzing the information contribution from each individual laser ray in a complete LIDAR scan, a subset is selected by maximizing the information content. It is shown how 80% of available LIDAR measurements can be dropped without significant loss of accuracy. Last, the problem of robustness in non-static environments is addressed. By extracting features from the LIDAR data, a computationally tractable localization method, resilient to errors in the map, is obtained. Moving objects, and tunnels being extended or closed, result in a map not corresponding to the LIDAR observations. State-of-the-art feature extraction methods for 2D LIDAR data are identified, and a localization algorithm is defined where features found in LIDAR data are matched to features extracted from the map. Experiments show that regions of the map containing errors are automatically ignored since no matching features are found in the LIDAR data, resulting in more robust position estimates. / <p>Additional funding agency: Epiroc Rock Drills AB</p>

5G offers several new improvements compared to its predecessors, such as higher speeds, lower latency and larger capacity. In order to ensure this, time of arrival algorithms are used in the Radio Access Networks for several purposes. Some of them are 3D positioning of User Equipment and synchronization. A big challenge for Time of Arrival algorithms are environments with multi-path propagation, especially when they use two-way measurements, as the algorithm may pick different propagation paths depending on the direction. This will result in a measurement error caused by asymmetry which leads to a degraded algorithm performance. This thesis investigates how time of arrival algorithms can be improved when facing asymmetry. Two different improvements are tested, the first is how using the same beamforming weights in the transmitter and the receiver impacts the assumption of channel reciprocity and the other is how the selection of the crosscorrelation peak can be altered. The simulations were performed using different Tapped Delay Line channel models in order to mimic a multipath propagation environment. The results show that the suggested peak search criterion could reduce measurement errors originating from asymmetry but also that the simulator was not equipped to test and verify that using the same beamforming weights would reduce asymmetry errors.

In real-time signal processing there is a constraint to finish processing of an audio signal before the next audio segment is received. This makes it important to have signal processing algorithms with low computational complexity while still maintaining high quality results. This thesis presents methods for audio signal processing used in real-time systems. The publications presented cover areas of noise reduction, network echo cancellation, noise dosimeter measurements and voice analysis. A method for speech enhancement is presented with low amounts of speech distortion. The audio signal is split into several subbands, covering different frequency regions. For each subband, the noise level is estimated. A signal gain is calculated by comparing the total signal level with the noise level for each subband. The method presented here, improves performance compared to previously similar methods. Improvement is especially found in multi-speaker and noise-only scenarios. When communicating on a telephone line, network echo is introduced by hybrids in the network. In cases where multiple echo sources exist, the time range for echoes can be quite long. In devices with limited storage, it is difficult to get good echo cancellation in such cases. This thesis presents a method for network echo cancellation suited for use in a device with a larger external memory. Exposure to high noise levels will have negative health effects and methods for measuring noise level exposure is important. Included in this thesis is a study that remove the influence of own voice in noise dose measurements. For certain medical conditions it is beneficial with daily voice exercises. Methods for grading voice in four different exercises is presented, based on pitch and loudness. Evaluation is done in real-time on test medical device.