GPS Kinematic Positioning for the Airborne Laser Altimetry at Long Valley

The object of this thesis is to develop a reliable algorithm and soft\vare for em-level kinematic GPS (Global Positioning System) data analysis. To assess the accuracy of the softvlare, we use it to determine the trajectory of the aircraft during the surveys at Long Valley, California, in 1993 and 1995. This thesis covers the algorithm development, the modeling, and the software design. We implement a robust Kalman filter to perfonn the kinematic data processing for GPS measurements. In the kinematic data processing with the Kalman filter, the estimates of the aircraft's position, the GPS receiver clock, and atmospheric corrections are modeled with appropriate stochastic processes. To achieve em-level accuracy for an aircraft trajectory, the GPS phase observables must be used and the integer-cycles of phase ambiguity must be resolved. In this thesis, we investigate the ambiguity problem in different situations and develop different ambiguity strategies depending on the situation. Firstly, we develop a position-independent (position-free) ambiguity search method for the initial ambiguity search for GPS kinematic surveying. Our ambiguity search method focuses on providing the flexibility and uniqueness to determine the correct ambiguities in most experimental conditions including long baselines (up to 100 km), high noise level in low elevation observations, and "bad" observations during the search. Secondly, we develop a method to utilize position-free widelane and extrawidelane observables to detect cycle slips that occur when the signal from a GPS satellite is interrupted during the flight, for example, when the satellite is blocked by the aircraft's wing during a turn. Our ambiguity algorithms use dual frequency GPS observables so that the effects of the ionospheric delay can be accounted for. Several tests perfonned indicate that our ambiguity strategy works well for a separation between the moving and fixed GPS receivers of up to 100 lan. We developed a killematic sofuvare developed to automatically detect various errors during the data processing, including detecting and correcting of cycle slips, detecting and removal of bad data, and performing ambiguity searches. 1be user interface to the software is command driven with default values for most processing. This interface provides flexibility and should make the software usable with little training. To evaluate our software, we processed GPS data taken in the 1993 and 1995 Long Valley airborne laser altimetry surveys. We performed four types of tests: (a) Static tests which