The optimization algorithm for blind processing of high frequency signal of capacitive sensor

Yuanjia Ma

doi:10.3934/dcdss.2019096

Article Contents

2019, Volume 12, Issue 4&5: 1399-1412. Doi: 10.3934/dcdss.2019096 open access

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The optimization algorithm for blind processing of high frequency signal of capacitive sensor

Yuanjia Ma^,

Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming, China

* Corresponding author: Yuanjia Ma
* Corresponding author: Yuanjia Ma

Received: August 2017

Revised: December 2017

Published: March 2019

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Abstract

At present, the high frequency signal processing algorithm of capacitive sensor based on RBF has the problems of poor filtering effect and high level of signal detection and poor quality of signal separation. In this paper, an optimization algorithm for blind processing of high frequency signal of capacitive sensor is proposed. Based on the gradient method, and the calculation way of improved variance gradient estimation, the gradient of square single- error sample is taken as the estimation of mean square error to filter the capacitive sensor signal, and adjust the filtering step by adjusting the threshold, which can enhance the filtering effect of the sensor signal; The detection threshold is calculated by determining the false alarm probability. The decision condition is used to detect the target signal and get the high accuracy sensor signal. The initialization separation matrix is set according to the number of observation signals, and the correlation matrix of the source signal can be calculated, so as to achieve the efficient separation of high frequency signals. The experiment shows that the algorithm can effectively solve the problems existing in the current signal processing algorithm, and it is reliable.

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Mathematics Subject Classification: 65D17.

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Figure 1. schematic diagram of adaptive filtering

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Figure 2. horizontal filter structure of joint parameter estimation

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Figure 3. The linear instantaneous aliasing model of the source signal

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Figure 4. experimental model

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Figure 5. Comparison of filtering effect of sensor operation by different algorithms

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Figure 6. Comparison of different algorithms for detecting high frequency signals of sensor

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Figure 7. Comparison of the separation effect of high frequency signals by different algorithms

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