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This paper presents a multiple model procedure to estimate the state of a ballistic object in the atmosphere and identify it using radar measurements for the purpose of impact point prediction (IPP). A key aspect of the projectile identification is the identification of the mode of stabilization used, i.e., fin stabilization or spin stabilization. Measurements are taken during the first part of its trajectory up to apogee, and the final state estimate obtained by the multiple model estimator is then predicted to its impact point on Earth. For each model a different extended Kalman filter (EKF) is used for state estimation, and the model likelihoods are then used to identify the projectile. It is shown from simulations carried out on three fin-stabilized projectile trajectories (mortars of different caliber) and a spin-stabilized (howitzer) projectile trajectory that the projectile can be identified with a high probability and also that the impact point is predicted to a high degree of accuracy and with a consistent covariance. It is also shown that accurate modeling of the gyroscopic effect caused by the spinning of the howitzer projectiles is critical for IPP accuracy in the case of spin-stabilized projectiles. The key in the design of the multiple model filter (MMF) is the choice of the models, which based on the characteristics of the different projectile trajectories, have different state dimensions. A choice has to be made between too few state components, which leads to poor accuracy/consistency, and too many state components, in which case the accuracy and discrimination ability suffers because of too much uncertainty in the model.