A fuzzy navigational state estimator for GPS/INS integration

Abstract

To achieve improved performance over a cascaded Kalman Global Positioning System (GPS)/Inertial Navigational System (INS) when biases are present, a fuzzy state estimator can be used. A Kalman filter assumes there is no bias in the measurement source, e.g., the GPS receiver. However, standard positioning system (SPS) GPS has selective availability (SA) and other satellite errors that translate into a bias associated with each satellite. This causes the GPS receiver position output to have a bias that is dependent upon which satellites are being used to determine the position. As the vehicle maneuvers, the GPS receiver uses different satellites, causing the bias to change. Another changing bias in a cascaded filter is the GPS receiver time lag. This time lag creates a bias that is dependent upon the vehicle's velocity and changes as the vehicle maneuvers. A standard Kalman filter will introduce errors into the filter's state velocities and positions as it tries to follow the GPS data with these changing biases. By using a fuzzy state estimator, which can incorporate heuristics into the filter that compensate for the changing biases, the errors in the state position and velocity due to these changing biases can be significantly reduced, providing a better performing state estimator for a SPS GPS/IRU system.