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

This issue Previous Article Intelligent recognition algorithm for social network sensitive information based on classification technology Next Article Multi-objective optimization algorithm based on improved particle swarm in cloud computing environment

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: July 31, 2017

Revised: November 30, 2017

Published: March 2019

Abstract Full Text(HTML) Figure(7) Related Papers Cited by

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.

Keywords:

Mathematics Subject Classification: 65D17.

Citation:

FullText(HTML)

Figure 1. schematic diagram of adaptive filtering

Download: Full-size image PowerPoint slide

Figure 2. horizontal filter structure of joint parameter estimation

Download: Full-size image PowerPoint slide

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

Download: Full-size image PowerPoint slide

Figure 4. experimental model

Download: Full-size image PowerPoint slide

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

Download: Full-size image PowerPoint slide

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

Download: Full-size image PowerPoint slide

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

Download: Full-size image PowerPoint slide

Related Papers

Cited by

References

[1]	G. A., Fabrication of fiber-optic distributed acoustic sensor and its signal processing, American Journal of Hypertension, 5 (2015), 483-491. doi: 10.1109/TSP.2012.2199314.
[2]	A. Bertrand and M. Moonen, Distributed canonical correlation analysis in wireless sensor networks with application to distributed blind source separation, IEEE Transactions on Signal Processing, 63 (2015), 4800-4813. doi: 10.1109/TSP.2015.2443729.
[3]	X. R. Chen, Nonlinear distortion suppression algorithm of complex optical sensor network communication, Bulletin of Science & http://ieeexplore.ieee.org/document/6200356/Technology, 58-60.
[4]	Y. Q. Chen, Stability of polytopic-type uncertain singular stochastic systems, Journal of Interdisciplinary Mathematics, 20 (2017), 47-62.
[5]	H. D., Y. K., X. L. and et al, Optimal parameter estimation under controlled communication over sensor networks, IEEE Transactions on Signal Processing, 63 (2015), 6473-6485. doi: 10.1109/TSP.2015.2469639.
[6]	J. Edwards, Signal processing powers a sensor revolution [special reports], IEEE Signal Processing Magazine, 33 (2016), 13-16.
[7]	F. Erden, S. Velipasalar, A. Z. Alkar and A. E. Cetin, Sensors in assisted living: A survey of signal and image processing methods, IEEE Signal Processing Magazine, 33 (2016), 36-44.
[8]	H. F., Intelligent sensor networks - the integration of sensor networks, signal processing and machine learning, Measurement Techniques, 535-537.
[9]	A. Gunes and M. B. Guldogan, Joint underwater target detection and tracking with the bernoulli filter using an acoustic vector sensor, Digital Signal Processing, 48 (2016), 246-258. doi: 10.1016/j.dsp.2015.09.020.
[10]	A. Hassani, A. Bertrand and M. Moonen, Gevd-based low-rank approximation for distributed adaptive node-specific signal estimation in wireless sensor networks, IEEE Transactions on Signal Processing, 64 (2016), 2557-2572. doi: 10.1109/TSP.2015.2510973.
[11]	S. P. Jia, J. Zeng and L. R. Guo, Designing implementation of signal sorting semi-physical simulation analysis platform, Journal of China Academy of Electronics & Information Technology, 59-65.
[12]	S. Kisseleff, I. F. Akyildiz and W. H. Gerstacker, Digital signal transmission in magnetic induction based wireless underground sensor networks, IEEE Transactions on Communications, 63 (2015), 2300-2311.
[13]	J. Li, H. Pang, F. Guo, L. Yang and W. Jiang, Localization of multiple disjoint sources with prior knowledge on source locations in the presence of sensor location errors, Digital Signal Processing, 40 (2015), 181-197. doi: 10.1016/j.dsp.2015.02.003.
[14]	H. L. Liu, Planning wetland ecology-based outdoor education courses in taiwanese junior high schools., Eurasia Journal of Mathematics Science & Technology Education, 13 (2017), 3261-3281.
[15]	B. M., C. D., M. A. and et al, Wavelet dt method for water leak-detection using a vibration sensor: an experimental analysis, Iet Signal Processing, 396-405.
[16]	J. Ma and S. Sun, Optimal linear estimators for multi-sensor stochastic uncertain systems with packet losses of both sides, Digital Signal Processing, 37 (2015), 24-34.
[17]	K. A. Mamun, C. M. Steele and T. Chau, Swallowing accelerometry signal feature variations with sensor displacement, Medical Engineering & Physics, 37 (2015), 665-673.
[18]	R. K. Miranda, J. P. C. L. D. Costa and F. Antreich, Low complexity performance assessment of a sensor array via unscented transformation, Digital Signal Processing, 190-198.
[19]	S. S. Q., L. J. Y., J. C. D. and et al, Least-square weighted smoothing filter technology applied in magnetic resonance sounding signal processing, Journal of Jilin University (Engineering and Technology Edition), 98 (2016), 985-995.
[20]	L. Staiger, On the hausdorff measure of regular omega-languages in cantor space, Discrete Mathematics and Theoretical Computer Science, 17 (1998), 357-368.
[21]	H. Y. Xiang, L. I. Ting-Ting, L. I. He and Y. Yang, Roller coaster acceleration signal processing based on matlab, Computer Simulation, 245-249.
[22]	H. Ying, L. Cheng-Chew and C. Sheng, Triple i fuzzy modus tollens method with inconsistent bipolarity information, Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 32 (2017), 4299-4309.
[23]	G. Zheng and B. Wu, Polarisation smoothing for coherent source direction finding with multiple-input and multiple-output electromagnetic vector sensor array, Iet Signal Processing, 10 (2016), 873-879.