An Analog Sensor Signal Processing Method Susceptible to Anthropogenic Noise Based on Improved Adaptive Singular Spectrum Analysis
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Summary
This summary is machine-generated.This study introduces an adaptive singular spectrum analysis (ASSA) algorithm using deep learning to improve sensor data processing. ASSA enhances accuracy and efficiency by automatically handling noise and interference, outperforming existing methods.
Area Of Science
- Signal Processing
- Geophysics
- Machine Learning
Background
- Sensor measurements are susceptible to complex noise, hindering signal processing.
- Singular Spectrum Analysis (SSA) faces challenges in determining decomposition layers, impacting precision and speed.
Purpose Of The Study
- To propose an improved adaptive singular spectrum analysis (ASSA) algorithm for enhanced sensor data processing.
- To address the limitations of traditional SSA, including parameter adjustment and processing time.
Main Methods
- Integration of a deep residual network (Res-Net) for automatic interference recognition.
- Development of a novel correlation detection reconstruction method using clustering for adaptive signal classification.
- Training the Res-Net with a comprehensive interference signal database.
Main Results
- The proposed ASSA algorithm achieved a Root Mean Square Error (RMSE) of 0.2 on magnetotelluric (MT) data.
- ASSA demonstrated a 14% improvement in accuracy compared to other signal extraction algorithms.
- The method effectively suppresses background noise and extracts meaningful signals.
Conclusions
- ASSA overcomes the challenge of determining optimal decomposition layers, eliminating manual parameter tuning.
- The algorithm significantly enhances the measurement efficiency and accuracy of sensor systems.
- ASSA shows potential for broad application in various data processing fields.
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