In this paper, a new motion-based approach for Global Positioning System (GPS) integer ambiguity resolution is derived. The approach first represents the GPS sightline vectors in the body frame or the baseline vectors in the reference frame. The solution to this problem is always available as long as at least three non-coplanar baseline or sightline vectors exist. The body-frame sightline or reference-frame baseline observations are the sum of two vectors, one depending on the phase measurements and the other on the unknown integers. The vector containing the integer phases is then converted into an attitude independent observation using scalar checking, which leads to a problem that treats the integers as to-be-determined biases. The bias estimation problem is typically solved by using a batch process. In this paper, simple real time algorithms are developed based on both the extended Kalman filter and Unscented filter. Simulation results indicate that both algorithms provide accurate integer resolution in real time, but the Unscented filter is more robust to large initial condition errors and slow vehicle motions than the extended Kalman filter.