Neural Network Tracking of Moving Objects with Unknown Equations of Motion
In this paper we present a Neural Network design that can be used to track the location of a moving object within a given range based on the object's noisy coordinates measurement. A function commonly performed by the KLMn filter, our goal is to show that our method outperforms the Kalman filter in certain scenarios.
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