A baseline drift removal algorithm based on cumulative sum and downsampling for hydroacoustic signal

Wu, Daiyue; Zhang, Guojun; Zhu, Shan; Liu, Yan; Liu, Guochang; Jia, Li; Yuding, Wu; Zhang, Wendong

doi:10.1016/j.measurement.2022.112344

Cited by 9 publications

(2 citation statements)

References 38 publications

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“…Traditional methods usually adopt circuit calibration to address baseline drift in signals, with high-pass filtering as a classical approach. However, highpass filtering can cause the distortion of low-frequency signals, and the additional electronic devices required can complicate circuit design and diminish system portability [9]. In addition to traditional methods, some time-series analysis methods have also been proven to be effective in removing noise from signals.…”

Section: Introductionmentioning

confidence: 99%

A Baseline Drift-Elimination Algorithm for Strain Measurement-System Signals Based on the Transformer Model

Wang,

Zhang,

et al. 2024

Applied Sciences

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Strain measurements are vital in engineering trials, testing, and scientific research. In the process of signal acquisition, baseline drift has a significant impact on the accuracy and validity of data. Traditional solutions, such as discrete wavelet transform and empirical mode decomposition, cannot be used in real-time systems. To solve this problem, this paper proposes a Transformer-based model to eliminate the drift in the signal. A self-attentive mechanism is utilized in the encoder of the model to learn the interrelationships between the components of the input signal, and captures the key features. Then, the decoder generates a corrected signal. Meanwhile, a high-precision strain acquisition system is constructed. The experiments tested the model’s ability to remove drift from simulated voltage signals with and without Gaussian noise. The results demonstrated that the transformer model excels at eliminating signal baseline drift. Additionally, the performance of the model was investigated under different temperature conditions and with different levels of force applied by the electronic universal testing machine to produce strain. The experimental results indicate that the Transformer model can largely eliminate drift in dynamic signals l and has great potential for practical applications.

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Section: Introductionmentioning

confidence: 99%

A Baseline Drift-Elimination Algorithm for Strain Measurement-System Signals Based on the Transformer Model

Wang,

Zhang,

et al. 2024

Applied Sciences

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“…Marine ambient noise interferes with the signal quality of feedphones and brings about a qualitative decline, and it has also brought great difficulties in accurate underwater target location (Wu et al , 2024). Therefore, it is crucial to preprocess the original signal to remove noise (Jia et al , 2023; Shang et al , 2023; Wu et al , 2023).…”

Section: Introductionmentioning

confidence: 99%

Improved DOA estimation of MEMS vector hydrophone combined with CEEMDAN and wavelet transform for noise reduction

Chang,

Zhang,

Zhang

et al. 2024

Self Cite

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Purpose Ciliated microelectromechanical system (MEMS) vector hydrophones pick up sound signals through Wheatstone bridge in cross beam-ciliated microstructures to achieve information transmission. This paper aims to overcome the complexity and variability of the marine environment and achieve accurate location of targets. In this paper, a new method for ocean noise denoising based on improved complete ensemble empirical mode decomposition with adaptive noise combined with wavelet threshold processing method (CEEMDAN-WT) is proposed. Design/methodology/approach Based on the CEEMDAN-WT method, the signal is decomposed into different intrinsic mode functions (IMFs), and relevant parameters are selected to obtain IMF denoised signals through WT method for the noisy mode components with low sample entropy. The final pure signal is obtained by reconstructing the unprocessed mode components and the denoising component, effectively separating the signal from the wave interference. Findings The three methods of empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and CEEMDAN are compared and analyzed by simulation. The simulation results show that the CEEMDAN method has higher signal-to-noise ratio and smaller reconstruction error than EMD and EEMD. The feasibility and practicability of the combined denoising method are verified by indoor and outdoor experiments, and the underwater acoustic experiment data after processing are combined beams. The problem of blurry left and right sides is solved, and the high precision orientation of the target is realized. Originality/value This algorithm provides a theoretical basis for MEMS hydrophones to achieve accurate target positioning in the ocean, and can be applied to the hardware design of sonobuoys, which is widely used in various underwater acoustic work.

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Baseline estimation using optimized asymmetric least squares (O-ALS)

Dong,

2024

Measurement

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A baseline drift removal algorithm based on cumulative sum and downsampling for hydroacoustic signal

Cited by 9 publications

References 38 publications

A Baseline Drift-Elimination Algorithm for Strain Measurement-System Signals Based on the Transformer Model

A Baseline Drift-Elimination Algorithm for Strain Measurement-System Signals Based on the Transformer Model

Improved DOA estimation of MEMS vector hydrophone combined with CEEMDAN and wavelet transform for noise reduction

Baseline estimation using optimized asymmetric least squares (O-ALS)

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