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Enhancing Ultrasonic Time-of-Flight Estimation Using Adaptive Differential Evolution and Levenberg-Marquardt Algorithm
A parameter estimation algorithm based on an asymmetric Gaussian model is proposed to address the problems of low accuracy and susceptibility to noise in traditional ultrasonic time-of-flight (TOF) estimation method, which improves the accuracy of TOF estimation and is less affected by noise. The al...
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Published in: | IEEE sensors journal 2024-01, Vol.24 (2), p.1-1 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | A parameter estimation algorithm based on an asymmetric Gaussian model is proposed to address the problems of low accuracy and susceptibility to noise in traditional ultrasonic time-of-flight (TOF) estimation method, which improves the accuracy of TOF estimation and is less affected by noise. The algorithm is a combination of adaptive differential evolution and Levenberg-Marquardt (LM), using the differential evolution algorithm as a global search strategy and the LM algorithm as a local search strategy. Firstly, the received echo signal is processed using Hilbert transform to extract the envelope, reducing dimensionality and computational burden. Then, the adaptive differential evolution algorithm converges quickly to a better solution, and then the result of the adaptive differential evolution algorithm is used as the initial value of the LM algorithm to further improve the accuracy, which solves the problem that the LM algorithm needs a good initial value to provide an accurate output. Finally, the model parameters are used to derive an accurate echo leading edge arrival time. The proposed algorithm is compared with several well-known DE variants as well as the latest algorithm in numerical simulations and ranging experiments. The results demonstrate the excellent performance of the ADE-LM algorithm in the estimation of ultrasonic TOF. |
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ISSN: | 1530-437X 1558-1748 |
DOI: | 10.1109/JSEN.2023.3324502 |