In this paper, we propose the use of subspace clustering to detect the states of dynamical systems from sequences of observations. In particular, we generate sparse and interpretable models that relate the states of aquatic drones involved in autonomous water monitoring to the properties (e.g., statistical distribution) of data collected by drone sensors. The subspace clustering algorithm used is called SubCMedians. A quantitative experimental analysis is performed to investigate the connections between i) learning parameters and performance, ii) noise in the data and performance. The clustering obtained with this analysis outperforms those generated by previous approaches.

Subspace clustering for situation assessment in aquatic drones: a sensitivity analysis for state-model improvement

A. Castellini;M. Bicego;D. Bloisi;J. Blum;F. Masillo;A. Farinelli
2019-01-01

Abstract

In this paper, we propose the use of subspace clustering to detect the states of dynamical systems from sequences of observations. In particular, we generate sparse and interpretable models that relate the states of aquatic drones involved in autonomous water monitoring to the properties (e.g., statistical distribution) of data collected by drone sensors. The subspace clustering algorithm used is called SubCMedians. A quantitative experimental analysis is performed to investigate the connections between i) learning parameters and performance, ii) noise in the data and performance. The clustering obtained with this analysis outperforms those generated by previous approaches.
Activity recognition, aquatic drones;autonomous vehicles, model interpretability, sensor data, situation assessment, subspace clustering, time series analysis, unsupervised learning, water monitoring
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1002928
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