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In the pursuit of further advancement in the field of target tracking, thispaper explores the efficacy of a feedforward neural network in predictingdrones tracks, aiming to eventually, compare the tracks created by thewell-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 usingonly a measurement model to estimate the next states of the track. Object modelselection and linearization is one of the challenges that always face in thetracking process. The neural network uses a sliding window to incorporate thehistory of measurements when applying estimations of the track values. Thetesting results are comparable to the ones generated by the Kalman filter,especially for the cases where there is low measurement covariance. Thecomplexity of linearization is avoided when using this proposed model.

Sliding Window Neural Generated Tracking Based on Measurement Model