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Tracking High-Speed Projectiles with an Event-Based Pipeline
The potential accuracy and reliability of event-based pipelines in light of their low-resource and lightweight physical demands make them promising candidates for critical systems with strict environmental constraints. The research done for this paper is intended to expand on event-based pipelines as an optimal means of tracking high-speed projectiles in real time. Time intervals between spikes in a neural network can be implemented in such a way that linear mathematical functions are predictable, as shown by Xavier Lagorce and Ryad Benosman's 2015 paper, "STICK: Spike Time Interval Computational Kernel, A Framework for General Purpose Computation using Neuron, Precise Timing, Delays and Synchrony." However, there has yet to be research on predicting non-linear functions with this method. In this work, an event-based sensor is used to gather high-speed projectile data, which is then processed to determine the optimal parameters for the ballistic equations. The specific spiking neural network is designed and integrated for further implementation in STICK. While smaller components of the ballistic functions are still necessary for the complete functionality of a STICK implementation to be applied to trajectories, this work provides proof of concept that the combination of these two technologies has the capability to allow for trajectory tracking without the current operational cost, constraints, and larger scale requirements of other current tracking techniques.
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