Real-time Adaptive Suppression of Broadband Noise in General Sensing Signals

WEN Yumei; ZHU Yu

doi:10.11999/JEIT250018

Volume 47 Issue 8

Aug. 2025

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WEN Yumei, ZHU Yu. Real-time Adaptive Suppression of Broadband Noise in General Sensing Signals[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2746-2756. doi: 10.11999/JEIT250018

Citation:

WEN Yumei, ZHU Yu. Real-time Adaptive Suppression of Broadband Noise in General Sensing Signals[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2746-2756. doi: 10.11999/JEIT250018

WEN Yumei, ZHU Yu. Real-time Adaptive Suppression of Broadband Noise in General Sensing Signals[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2746-2756. doi: 10.11999/JEIT250018

Citation:

WEN Yumei, ZHU Yu. Real-time Adaptive Suppression of Broadband Noise in General Sensing Signals[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2746-2756. doi: 10.11999/JEIT250018

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Real-time Adaptive Suppression of Broadband Noise in General Sensing Signals

doi: 10.11999/JEIT250018 cstr: 32379.14.JEIT250018

WEN Yumei^{1, 2
,
,},
ZHU Yu¹

1.
School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, China
2.
State Key Laboratory of Submarine Geoscience, Shanghai 200240, China

Funds: The National Key R&D Program of China (2021YFC2202803)

Received Date: 2025-01-10
Rev Recd Date: 2025-03-31

Available Online: 2025-04-15

Publish Date: 2025-08-27

Abstract

Abstract

Objective Broadband noise is inevitable in sensing outputs due to thermal noise from the sensing system and various uncorrelated environmental disturbances. Adaptive filtering is a common method for removing such noise. At convergence, the adaptive filter output provides the optimal estimate of the sensing signal. However, during actual sensing, changes in the sensing signal lead to alterations in the statistical characteristics of the output. Therefore, the adaptive process must be re-adjusted to converge to a new steady state. The filter output during this adjustment is not the optimal estimate and introduces distortion, thereby adding extra noise. Fast-converging adaptive algorithms are typically employed to improve the filter’s response speed to such changes. Despite the speed of convergence and the methods used to update filter coefficients, the adjustment process remains unavoidable, during which the filter output is distorted, and additional noise is introduced. To ensure the filter remains at steady state without being influenced by changes in the sensing signal, a new adaptive filtering method is proposed. This method ensures that the input to the adaptive filter remains stationary, thereby preventing output distortion and the introduction of extra noise. Methods First, a threshold $ R $ and quantization scale $ Q $ are defined in terms of the noise standard deviation, $ \sigma $, where $ R = 3\sqrt 2 \sigma $ and $ Q = 3\sigma $. A quantization transformation is applied to the sensing output $ x(n) $ in real time, with the transformation result $ q(n) $ used as the new sequence to be filtered. When the absolute value of the first-order difference of $ x(n) $ is no less than $ R $, the sensing signal $ s(n) $ is considered to have changed, and $ p(n) $ is set as the quantization value of $ x(n) $ according to $ Q $. When the absolute value of the first-order difference of $ x(n) $ is less than $ R $, $ s(n) $ is considered unchanged, and $ p(n) $ is equal to the previous value, i.e., $ p(n) = p(n - 1) $. Let $ q(n) = x(n) - p(n) $, $ q(n) $ contains both the information of the sensing signal and the noise. Although its variance may change slightly, the mean of $ q(n) $ remains 0, ensuring that $ q(n) $ stays relatively stationary. Next, $ q(n - {n_0}) $ is used as the input to the adaptive filter, with $ q(n) $ serving as the reference for the adaptive filter. Here, $ q(n - {n_0}) $ represents the time delay of $ q(n) $ and $ {n_0} $ denotes the length of the time delay. This method performs adaptive linear prediction of $ q(n) $ and filters out broadband noise. Finally, the output of the adaptive filter, $ y(n) $, is compensated with $ p(n) $ to obtain an estimation of the sensing signal $ s(n) $ by removing noise. Results and Discussions The maximum mean square errors produced by the proposed method and conventional adaptive algorithms are compared using computer-simulated noisy band-limited step signals and noisy one-sided sinusoidal signals. Additionally, Signal-to-Noise Ratio (SNR) improvements obtained during filtering are also evaluated concurrently. For the noisy band-limited step signal (Table 1), the maximum mean square error of the proposed method is only 0.18% of that produced by the Recursive Least Squares (RLS) algorithm and 0.15%～0.19% of those generated by the Least Mean Square (LMS) algorithms. Correspondingly, the SNR improvement is 25.88 dB higher than the RLS algorithm and between 28.65 dB and 32.35 dB greater than the LMS algorithms. In processing a noisy one-sided sinusoidal signal (Table 2), the maximum mean square error generated by the proposed method is 0.3% of that generated by the RLS algorithm and 0.06%～0.08% of that generated by the compared LMS algorithms. The SNR improvement is 10.25 dB higher than that of the RLS algorithm and 26.53 dB～29.61 dB higher than that of the compared LMS algorithms. Figures 3 and 5 illustrate the quantization transformation outcomes for both the noisy band-limited step signal and noisy sinusoidal signal, demonstrating stability and consistency with theoretical expectations. Real sensing outputs primarily cover static or quasi-static signals (Figures 7 and 8); step or step-like signals (Figures 9 and 10), and periodic or quasi-periodic signals (Figures 11 and 12). Comparative analysis of the proposed method against common adaptive algorithms on varied real sensing outputs consistently shows superior filtering performance by the proposed method, with minimal distortion and no additional noise introduction, regardless of whether the sensing signals undergo changes. Conclusions A new adaptive filtering method is proposed in this paper. The proposed method ensures that the adaptive filter always operates at a steady state, avoiding the introduction of additional noise caused by distortion during the adjustment to the new steady state. The results from computer simulations and actual signal processing demonstrate that the proposed method provides effective filtering for both dynamic and static sensing signals, indicating that it outperforms commonly used adaptive algorithms.
- Broadband noise,
- Noise suppression,
- Quantization,
- Real-time filtering,
- Adaptive algorithm

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References(22)

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