Studies on Sensor Aided Positioning and Context Awareness

This thesis studies Global Navigation Satellite Systems (GNSS) in combination with sensor systems that can be used for positioning and obtaining richer context information. When a GNSS is integrated with sensors, such as accelerometers, gyroscopes and barometric altimeters, valuable information can be produced for several applications; for example availability or/and performance of the navigation system can be increased. In addition to position technologies, GNSS devices are integrated more often with different types of technologies to fulfil several needs, e.g., different types of context recognition. The most common integrated devices are accelerometer, gyroscope, and magnetometer but also other sensors could be used.More specifically, this thesis presents sensor aided positioning with two satellite signals with altitude assistance. The method uses both pseudorange and Doppler measurements. The system is required to be stationary during the process and a source of altitude information, e.g., a MEMS barometer, is needed in addition to a basic GNSS receiver. Authentic pseudorange and Doppler measurements with simulated altitude were used used to test the algorithm. Results showed that normally the accuracy of couple of kilometers is acquired. Thesis also studies on what kind of errors barometric altimeter might encounter especially in personal positioning. The results show that barometers in differential mode provide highly accurate altitude solution (within tens of centimeters), but local disturbances in pressure need to be acknowledged in the application design. For example, heating, ventilating, and air conditioning in a car can have effect of few meters. Thus this could cause problems if the barometer is used as a altimeter for under meter-level positioning or navigation.We also explore methods for sensor aided GNSS systems for context recognition. First, the activity and environment recognition from mobile phone sensor and radio receiver data is investigated. The aim is in activity (e.g., walking, running, or driving a vehicle) and environment (e.g., street, home, or restaurant) detection. The thesis introduces an algorithm for user specific adaptation of the context model parameters using the feedback from the user, which can provide a confidence measure about the correctness of a classification. A real-life data collection campaign validate the proposed method. In addition, the thesis presents a concept for automated crash detection to motorcycles. In this concept, three different inertial measurement units are attached to the motorist’s helmet, torso of the motorist, and to the rear of the motor cycle. A maximum a posteriori classifier is trained to classify the crash and normal driving. Crash dummy tests were done by throwing the dummy from different altitudes to simulate the effect of crash to the motorist and real data is collected by driving the motorcycle. Preliminary results proved the potential of the proposed method could be applicable in real situations. In all the proposed systems in this thesis, knowledge of the context can help the positioning system, but also positioning system can help in determining the context

Antenna Steering System For Directional Microwave Link With UAV Communications

In the past few years, new uses for UAV (Unmanned Aerial Vehicles) have been discovered and deeply developed due to the potential advantages this technology can offer in the modern world. One attractive use for this aerial technology is the carriage of LTE stations to provide temporary wireless coverage in certain scenarios. Necessary information for the proper operation of the system is exchanged through a key backbone link established between UAV and a ground station (GS). However, with current designs, this link can only be established for small distances. For many applications concerning the use of UAVs, a backbone link working at medium-high distances could be really useful. The goal of this thesis is designing a steering algorithm to make possible a future enlargement of this key link. To make this possible, two high gain, high directivity antennas are used. The first one will be installed in the UAV, and the second one in the ground station. Behind these devices, a steering system will be integrated to point them properly to each other. This way, the link will be operative all the time. Along this thesis, the design of the software algorithm providing the angles to point the antennas have been developed. It is also explained the configuration of the servo motors to provide the desired angles. As a result, it is obtained an accurate steering system providing the values of elevation and azimuth for both antennas. It is only left to attach the servo motors to a mechanical structure to obtain the final rotation. With the steering system developed within this research thesis, many kinds of am- ateur and professional aerial vehicles can be operated from medium-high distances. The system provides a complete autonomous solution, avoiding the need of any kind of human iteration, which makes the method transparent for the final user

UAV or Drones for Remote Sensing Applications in GPS/GNSS Enabled and GPS/GNSS Denied Environments

The design of novel UAV systems and the use of UAV platforms integrated with robotic sensing and imaging techniques, as well as the development of processing workflows and the capacity of ultra-high temporal and spatial resolution data, have enabled a rapid uptake of UAVs and drones across several industries and application domains.This book provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of single- and multiple-UAV developments for remote sensing applications, and associated developments in sensor technology, data processing and communications, and UAV system design and sensing capabilities in GPS-enabled and, more broadly, Global Navigation Satellite System (GNSS)-enabled and GPS/GNSS-denied environments.Contributions include:UAV-based photogrammetry, laser scanning, multispectral imaging, hyperspectral imaging, and thermal imaging;UAV sensor applications; spatial ecology; pest detection; reef; forestry; volcanology; precision agriculture wildlife species tracking; search and rescue; target tracking; atmosphere monitoring; chemical, biological, and natural disaster phenomena; fire prevention, flood prevention; volcanic monitoring; pollution monitoring; microclimates; and land use;Wildlife and target detection and recognition from UAV imagery using deep learning and machine learning techniques;UAV-based change detection