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This paper presents a deep learning approach to estimate a projectile trajectory in a GNSS-denied environment. For this purpose, Long-Short-Term-Memories (LSTMs) are trained on projectile fire simulations. The network inputs are the embedded Inertial Measurement Unit (IMU) data, the magnetic field reference, flight parameters specific to the projectile and a time vector. This paper focuses on the influence of LSTM input data pre-processing, i.e., normalization and navigation frame rotation, leading to rescale 3D projectile data over similar variation ranges. In addition, the effect of the sensor error model on the estimation accuracy is analyzed. LSTM estimates are compared to a classical Dead-Reckoning algorithm, and the estimation accuracy is evaluated via multiple error criteria and the position errors at the impact point. Results, presented for a finned projectile, clearly show the Artificial Intelligence (AI) contribution, especially for the projectile position and velocity estimations. Indeed, the LSTM estimation errors are reduced compared to a classical navigation algorithm as well as to GNSS-guided finned projectiles. [ABSTRACT FROM AUTHOR]

Highlights: [•] A multi-level multi-sensor fusion for speed limit assistance is presented. [•] A geographic information system and a traffic sign recognition system are fused in the TBM. [•] The sources’ discordance management is proposed using a conflict-related decision rule. [•] The decentralized structure corrects the sensor errors by an intra-sensor decision. [•] Qualitative and quantitative analysis show the benefits of the proposed solution. [Copyright &y& Elsevier]