Recurrent Neural Network based Sensor Fusion Algorithm for Alternative Position, Navigation and Timing

A new sensor fusion algorithm for Alternative Position, Navigation and Timing (APNT) is designed with deep Recurrent Neural Network (RNN) using Long Short-Term Memory (LSTM). The proposed deep RNN for APNT estimates the position of the Unmanned Aerial Vehicle (UAV) using the UAV position measurements from the holographic radar and the Radio Positioning System (RPS). For the training dataset generation, flight simulations with multiple episodes are conducted with the measurement models of the holographic radar and the RPS. The testing results of the well-trained deep RNN are provided for verification and validation of the proposed deep RNN. The advantage of the proposed deep RNN over the Extended Kalman Filter (EKF) which is a conventional sensor fusion algorithm is demonstrated by comparing their testing results.