In tightly coupled GPS/INS integration, the data fusion algorithm is faced with nonlinear system dynamics and measurement models. The extended Kalman filter (EKF) approximates the propagation of Gaussian random vectors through these nonlinear equations using a linear transformation. This captures mean and covariance to first order accuracy only. Sigma‐point Kalman filters (SPKF) offer third order accuracy. In this paper, the performance of EKF‐based and SPKF‐based tightly coupled GPS/INS systems is compared in numerical simulations. The simulation results were confirmed by post‐processing raw GPS and inertial sensor data that was recorded during a test drive. It was found that, except for specific situations without practical relevance, EKF and SPKF offer an identical performance. This is due to the fact that for tightly coupled—as well as loosely coupled—GPS/INS integration, the higher‐order transformation terms are negligible. This result was shown analytically.

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