Adaptive Time Delay Estimation Based on Signal Preprocessing and Fourth-Order Cumulant

Liu, Baoheng; Zhang, Xiaochuan; Jia, Shuyang; Zou, Sichen; Tian, Deyan

doi:10.1007/s00034-023-02390-7

Cited by 1 publication

(1 citation statement)

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“…Researchers have proposed the adaptive filtering technique to improve the signal-to-noise ratio of weak signals in complex noise environments. Reference [1] introduces an adaptive time delay estimation algorithm for low-signal-to-noise-ratio environments that can accurately estimate the filtering results. Reference [2] proposed a method combining LMS and RLS filters, which is able to obtain results close to Kalman filtering performance with low computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of DSLMS Algorithm Based Photoelectric Detection of Weak Signals

Wang,

Song

et al. 2024

Applied Sciences

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Accurately extracting weak signals is extremely important for overall performance and application in optoelectronic imaging and optical communication systems. While weak signals are susceptible to noise, adaptive filtering is a commonly used noise removal method. Still, its convergence speed is slow, the steady-state error is large, and the anti-interference ability is weak. To solve the above problems, this paper proposes a new type of variable-step-length adaptive filtering algorithm (DSLMS) based on the minutiae function, which effectively reduces the noise component in error through its combination with the pair cancelation system, utilizing the low correlation property of the noise signal, to improve the anti-noise interference ability of the adaptive filter. Using FPGA and Matlab (2018b) for experimental verification, the results show that this algorithm shows significant advantages in noise suppression, accelerated algorithm convergence, and low steady-state error, and it has effectiveness and application potential for the optoelectronic detection of weak signal processing.

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Section: Introductionmentioning

confidence: 99%