In this note we focus on convergence behavior of the Extended Kalman Filter used as a state estimator for
projectile attitude and position estimation. We provide first the complete dynamical model, into a non linear state space
form, to describe the projectile behavior. Due to strong non linearities and poor observability of the system, very few
estimation techniques could be applied, among them the celebrate EKF. The later is, however, very sensitive to bad
initializations and small perturbations. The main contribution of this work lies in the use of a modified EKF to assure a
strong tracking using magnetometer sensor only. The modified EKF follows from the connection of some instrumental
matrices, fixed by the user, and the convergence behavior. Simulation results show the good performances of the
A priori information given by the complete modelling of the ballistic behavior (trajectory, attitude) of the projectile is simplified to give a pertinent reduced evolution model. An algorithm based on extended Kalman filters is designed to determinate: • position: x,y,z references in earth frame. • value and direction of the velocity vector; its direction is given by 2 angles (η and θ). • attitude around velocity vector given by 3 angles: roll angle in the range [0, 2π], angle of attack α and side-slip angle β in the range of few milliradians. The estimation is based on the measures of the magnetic field of the earth given by a three-axis magnetometer sensor embedded on the projectile. The algorithm also needs the knowledge of the direction of the earth magnetic fields in the earth frame and aerodynamics coefficients of the projectile. The algorithm has been tested on simulation, using real evolution of attitude data for a shot with a 155 mm rotating projectile over a distance of 16 km, with wind and measurement noise. The results show that we can estimate milliradians with non-linear equations and approximations, with good precision.