This paper discusses the performance improvement that a neural network can provide to a contactless distance sensor based on the measurement of the time of flight (TOF) of an ultrasonic (US) pulse. The sensor, which embeds a correction system for the temperature effect, achieves a distance uncertainty (rms) of less than 0.5 mm over 0.5 m by using a two-level neural network to process the US echo and determine the TOF in the presence of environmental acoustic noise. The network embeds a "guard" neuron that guards against gross measurement errors, which would be possible in the presence of high environmental noise.

Ultrasonic distance sensor improvement using a two-level neural network / Carullo, Alessio; Ferraris, Franco; Graziani, S.; Grimaldi, U.; Parvis, Marco. - In: IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT. - ISSN 0018-9456. - STAMPA. - 45:2(1996), pp. 677-682. [10.1109/19.492808]

Ultrasonic distance sensor improvement using a two-level neural network

CARULLO, Alessio;FERRARIS, Franco;PARVIS, Marco
1996

Abstract

This paper discusses the performance improvement that a neural network can provide to a contactless distance sensor based on the measurement of the time of flight (TOF) of an ultrasonic (US) pulse. The sensor, which embeds a correction system for the temperature effect, achieves a distance uncertainty (rms) of less than 0.5 mm over 0.5 m by using a two-level neural network to process the US echo and determine the TOF in the presence of environmental acoustic noise. The network embeds a "guard" neuron that guards against gross measurement errors, which would be possible in the presence of high environmental noise.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/1400293
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