Which method handles nonlinear dynamics by linearizing around the current state in INS/GNSS fusion?

• A. Extended Kalman Filter (EKF)
• B. Unscented Kalman Filter (UKF)
• C. Kalman Filter without adaptation
• D. Particle Filter

Answer: (A)

The method uses linearization around the current state to handle nonlinear dynamics. In INS/GNSS fusion, the motion and measurement models are nonlinear, so this approach approximates them with a first-order Taylor expansion around the current state estimate. By computing the Jacobian of the dynamics with respect to the state (F_x) and using it to propagate the error covariance, the estimator can apply the Kalman update despite nonlinearity. This exact strategy—linearizing around the present estimate to keep the familiar Kalman filter framework—is what the extended Kalman filter does.

Other options either avoid linearization (the unscented approach uses the unscented transform), assume linear dynamics (a linear Kalman filter with no adaptation), or rely on sampling to handle nonlinearities (particle filter). But the described method of linearizing around the current state within the Kalman framework is characteristic of the extended Kalman filter.