An Analog Sensor Signal Processing Method Susceptible to Anthropogenic Noise Based on Improved Adaptive Singular Spectrum Analysis
Zhengyang Gao, Shuangchao Ge, Jie Li, Wentao Huang, Kaiqiang Feng, Chenming Zhang, Chunxing Zhang, Jiaxin Sun
TL;DR
This paper introduces an improved signal processing method using adaptive singular spectrum analysis and deep learning to better handle noise in sensor data.
Contribution
The novel contribution is an adaptive singular spectrum analysis algorithm enhanced with a deep residual network for automatic interference recognition and signal extraction.
Findings
The improved ASSA algorithm achieved an RMSE of 0.2 in processing magnetotelluric observation data.
ASSA improved accuracy by 14% compared to other signal extraction algorithms.
The method eliminates the need for manual parameter adjustment, enhancing sensor system efficiency.
Abstract
Sensor measurements are often affected by complex ambient noise and complicating signal processing tasks. The singular spectrum decomposition (SSA) algorithm, while widely used, faces challenges such as the difficulty of determining the number of decomposition layers, requiring iterative adjustments that reduce precision and increase processing time. This paper proposes an improved adaptive singular spectrum analysis (ASSA) algorithm that integrates a deep residual network (Res-Net) for automatic recognition. A comprehensive interference signal database was constructed to train the Deep Res-Net, and common interferences were restored through the combination of different signals, enabling greater frequency resolution performance. Meanwhile, a novel correlation detection reconstruction method based on a clustering algorithm for adaptive signal classification was developed to suppress…
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Taxonomy
TopicsStatistical and numerical algorithms · Geophysical and Geoelectrical Methods · Inertial Sensor and Navigation