Sliding Window Neural Generated Tracking Based on Measurement Model
Haya Ejjawi Amal El Fallah Seghrouchni Frederic Barbaresco Raed Abu Zitar
In the pursuit of further advancement in the field of target tracking, this
paper explores the efficacy of a feedforward neural network in predicting
drones tracks, aiming to eventually, compare the tracks created by the
well-known Kalman filter and the ones created by our proposed neural network.
The unique feature of our proposed neural network tracker is that it is using
only a measurement model to estimate the next states of the track. Object model
selection and linearization is one of the challenges that always face in the
tracking process. The neural network uses a sliding window to incorporate the
history of measurements when applying estimations of the track values. The
testing results are comparable to the ones generated by the Kalman filter,
especially for the cases where there is low measurement covariance. The
complexity of linearization is avoided when using this proposed model.