In the generation of the standard GRACE gravity fields, the K-Band Ranging (KBR) system data is used in its range-rate mode. Because time derivatives attenuate the gravity signal relative to the data noise at the lower frequencies, it is thought that solutions using range data might have better low-degree (low-frequency) characteristics. The purpose of this work is to detail the methods required to generate range-based solutions, to determine some of the properties of these solutions and then to compare them to range-rate based solutions. It is demonstrated that the range-based solutions are feasible. Different subarc lengths and parameterizations were considered. Although, the most effective combination of subarc lengths and parameterizations are not picked, it is concluded that estimating the mixed periodic term along with bias, bias-rate, bias-acceleration and periodic terms degrades the quality of the range based solution and therefore should not be used. Further study is necessary to pick the optimal combination of subarc length and parameterization which would be used in the time-series analysis. / text

International Telemetering Conference Proceedings / October 28-31, 1985 / Riviera Hotel, Las Vegas, Nevada / Digital processing techniques and related algorithms for receiving and processing space vehicle downlink signals are discussed. The combination of low minimum signal to noise density (C/No), large signal dynamic range, unknown time of arrival, and high space vehicle dynamics that is characteristic of some of these downlink signals results in a difficult acquisition problem. A method for rapid acquisition is described which employs a Fast Fourier Transform (FFT). Also discussed are digital techniques for precise measurement of space vehicle range and range rate using a digitally synthesized number controlled oscillator (NCO).

Autonomous features in vehicles is already a big part of the automobile area and now many companies are looking for ways to make vehicles fully autonomous. Autonomous vehicles need to get information about the surrounding environment. The information is extracted from exteroceptive sensors and today vehicles often use laser scanners for this purpose. Laser scanners are very expensive and fragile, it is therefore interesting to investigate if cheaper radar sensors could be used. One big challenge when it comes to autonomous vehicles is to be able to use the exteroceptive sensors and extract a position of the vehicle and at the same time get a map of the environment. The area of Simultaneous Localization and Mapping (SLAM) is a well explored area when using laser scanners but is not that well explored when using radars. It has been investigated if it is possible to use radar sensors on a truck to create a map of the area where the truck drives. The truck has been equipped with ego-motion sensors and radars and the data from them has been fused together to get a position of the truck and to get a map of the surrounding environment, i.e. a SLAM algorithm has been implemented. The map is represented by an Occupancy Grid Map (OGM) which should only consist of static objects. The OGM is updated probabilistically by using a binary Bayes filter. To localize the truck with help of motion sensors an Extended Kalman Filter (EKF) is used together with a map and a scan match method. All these methods are put together to create a SLAM algorithm. A range rate filter method is used to filter out noise and non-static measurements from the radar. The results of this thesis show that it is possible to use radar sensors to create a map of a truck's surroundings. The quality of the map is considered to be good and details such as space between parked trucks, signs and light posts can be distinguished. It has also been proven that methods with low performance on their own can together with other methods work very well in the SLAM algorithm. Overall the SLAM algorithm works well but when driving in unexplored areas with a low number of objects problems with positioning might occur. A real time system has also been implemented and the map can be seen at the same time as the truck is manoeuvred.