초록

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Abstract ▼ AI-Helper

Determination of position, velocity and time from the satellite signals is the central problem of the GPS receiver. Generally, GPS receiver uses least square method for navigation filter algorithm. The Kalman filter has known as an optimal linear estimator, which uses the knowledge of the system dyn...

Determination of position, velocity and time from the satellite signals is the central problem of the GPS receiver. Generally, GPS receiver uses least square method for navigation filter algorithm. The Kalman filter has known as an optimal linear estimator, which uses the knowledge of the system dynamics and the statistical characteristics of the system noises and measurement error. In this paper, we design an 8 state Kalman filter for GPS navigation and employ some techniques that can reduce computational burden, increase numerical stability, and improve filtering performance. To show its effectiveness, the designed filter is implemented in a GPS receiver and tested in real-time.