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Adaptation Using System Identification to Improve Electrolocation


Electrolocation is a method of sensing found in weakly electric fish that utilizes electrical discharges to sense objects and navigate through their environment. Inspired by these biological findings, this thesis will describe a modeling and processing method to emulate an application using this sensing modality. We approached the problem with System Identification to estimate a non-parametric model instead of relying on complex physical equations. We will describe how a Kalman filter and an estimation function uses the model to process incoming sensory information. From our analysis, we show improvements using online adaptive model over offline training. Finally we will compare experimental results estimating object distance using linear regression and SVM.

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