Integration of Differential GPS and Inertial Navigation using a Complementary Kalman Filter.

摘要

Precise navigation with high update rates is essential for automatic landing of an unmanned aircraft. Individual sensors currently available - INS, AHRS, GPS, LORAN, etc. - cannot meet both requirements. The most accurate navigation sensor available today is the Global Positioning System or GPS. However, GPS updates only come once per second. INS, being an on-board sensor, is available as often as necessary. Unfortunately, it is subject to the Schuler cycle, biases, noise floor, and cross-axis sensitivity. In order to design and verify a precise, high update rate navigation system, a working model of Differential GPS has been developed including all of the major GPS error sources - clock differences, atmospherics, selective availability and receiver noise. A standard INS system was also modeled, complete with the inaccuracies mentioned. The outputs of these two sensors - inertial acceleration and pseudoranges - can be optimally blended with a complementary Kalman filter for positioning. Eventually, in the discrete case, the high update rate and high precision required for autoland can be achieved. Kalman filtering, Differential global position system, Inertial navigation system, Modeling.