Research on RTD Fluxgate Induction Signal Denoising Method Based on Particle Swarm Optimization Wavelet Neural Network
Aeromagnetic surveying technology detects minute variations in Earth’s magnetic field and is essential for geological studies, environmental monitoring, and resource exploration. Compared to conventional methods, residence time difference (RTD) fluxgate sensors deployed on unmanned aerial vehicles (...
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/482 |
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| author | Xu Hu Na Pang Haibo Guo Rui Wang Fei Li Guo Li |
| author_facet | Xu Hu Na Pang Haibo Guo Rui Wang Fei Li Guo Li |
| author_sort | Xu Hu |
| collection | DOAJ |
| description | Aeromagnetic surveying technology detects minute variations in Earth’s magnetic field and is essential for geological studies, environmental monitoring, and resource exploration. Compared to conventional methods, residence time difference (RTD) fluxgate sensors deployed on unmanned aerial vehicles (UAVs) offer increased flexibility in complex terrains. However, measurement accuracy and reliability are adversely affected by environmental and sensor noise, including Barkhausen noise. Therefore, we proposed a novel denoising method that integrates Particle Swarm Optimization (PSO) with Wavelet Neural Networks, enhanced by a dynamic compression factor and an adaptive adjustment strategy. This approach leverages PSO to fine-tune the Wavelet Neural Network parameters in real time, significantly improving denoising performance and computational efficiency. Experimental results indicate that, compared to conventional wavelet transform methods, this approach reduces time difference fluctuation by 23.26%, enhances the signal-to-noise ratio (SNR) by 0.46%, and improves sensor precision and stability. This novel approach to processing RTD fluxgate sensor signals not only strengthens noise suppression and measurement accuracy but also holds significant potential for improving UAV-based geological surveying and environmental monitoring in challenging terrains. |
| format | Article |
| id | doaj-art-9b1e9f16d6344ca2ad1b6cd8e055920c |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-9b1e9f16d6344ca2ad1b6cd8e055920c2025-01-24T13:49:05ZengMDPI AGSensors1424-82202025-01-0125248210.3390/s25020482Research on RTD Fluxgate Induction Signal Denoising Method Based on Particle Swarm Optimization Wavelet Neural NetworkXu Hu0Na Pang1Haibo Guo2Rui Wang3Fei Li4Guo Li5College of Computer Science and Technology, Beihua University, No. 3999 East Binjiang Road, Jilin 132013, ChinaCollege of Computer Science and Technology, Beihua University, No. 3999 East Binjiang Road, Jilin 132013, ChinaCollege of Computer Science and Technology, Beihua University, No. 3999 East Binjiang Road, Jilin 132013, ChinaCollege of Computer Science and Technology, Beihua University, No. 3999 East Binjiang Road, Jilin 132013, ChinaCollege of Computer Science and Technology, Beihua University, No. 3999 East Binjiang Road, Jilin 132013, ChinaCollege of Computer Science and Technology, Beihua University, No. 3999 East Binjiang Road, Jilin 132013, ChinaAeromagnetic surveying technology detects minute variations in Earth’s magnetic field and is essential for geological studies, environmental monitoring, and resource exploration. Compared to conventional methods, residence time difference (RTD) fluxgate sensors deployed on unmanned aerial vehicles (UAVs) offer increased flexibility in complex terrains. However, measurement accuracy and reliability are adversely affected by environmental and sensor noise, including Barkhausen noise. Therefore, we proposed a novel denoising method that integrates Particle Swarm Optimization (PSO) with Wavelet Neural Networks, enhanced by a dynamic compression factor and an adaptive adjustment strategy. This approach leverages PSO to fine-tune the Wavelet Neural Network parameters in real time, significantly improving denoising performance and computational efficiency. Experimental results indicate that, compared to conventional wavelet transform methods, this approach reduces time difference fluctuation by 23.26%, enhances the signal-to-noise ratio (SNR) by 0.46%, and improves sensor precision and stability. This novel approach to processing RTD fluxgate sensor signals not only strengthens noise suppression and measurement accuracy but also holds significant potential for improving UAV-based geological surveying and environmental monitoring in challenging terrains.https://www.mdpi.com/1424-8220/25/2/482RTD fluxgate sensorparticle swarm optimizationwavelet neural networknoise suppression |
| spellingShingle | Xu Hu Na Pang Haibo Guo Rui Wang Fei Li Guo Li Research on RTD Fluxgate Induction Signal Denoising Method Based on Particle Swarm Optimization Wavelet Neural Network Sensors RTD fluxgate sensor particle swarm optimization wavelet neural network noise suppression |
| title | Research on RTD Fluxgate Induction Signal Denoising Method Based on Particle Swarm Optimization Wavelet Neural Network |
| title_full | Research on RTD Fluxgate Induction Signal Denoising Method Based on Particle Swarm Optimization Wavelet Neural Network |
| title_fullStr | Research on RTD Fluxgate Induction Signal Denoising Method Based on Particle Swarm Optimization Wavelet Neural Network |
| title_full_unstemmed | Research on RTD Fluxgate Induction Signal Denoising Method Based on Particle Swarm Optimization Wavelet Neural Network |
| title_short | Research on RTD Fluxgate Induction Signal Denoising Method Based on Particle Swarm Optimization Wavelet Neural Network |
| title_sort | research on rtd fluxgate induction signal denoising method based on particle swarm optimization wavelet neural network |
| topic | RTD fluxgate sensor particle swarm optimization wavelet neural network noise suppression |
| url | https://www.mdpi.com/1424-8220/25/2/482 |
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