Earthquake ground motion information is useful for structural vibration control during earthquakes and post-earthquake inspections of structural damage. However, in practice, the ground input data on a specific location may not be available because of instrumentation limitations or technical issues. Numerous studies have been devoted to identifying the unknown excitation. Most of these studies required responses in relative coordinates or from systems with direct feedthrough. On the other hand, acceleration monitoring systems in applications measure global responses of buildings, which corresponds to the absolute acceleration of a point on a structure, that is, the measured floor acceleration is the sum of the unknown earthquake-induced ground motion and acceleration relative to the ground when an earthquake happens. In this study, a ground motion estimation approach was developed based on an extended Kalman filter (EKF) with an embedded Bayesian noise parameter updating technique, which requires a relatively small number of sensors for absolute acceleration measurements. Numerical simulations, a shaking table test, and a real-world application were performed to demonstrate the applicability and accuracy of the proposed method.