Comparative study of five LMS-based adaptive time delay estimators

So, Hing Cheung; Ching, P.C.

doi:10.1049/ip-rsn:20010145

Cited by 30 publications

(16 citation statements)

References 19 publications

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“…The third-order cumulant of X ( n ) is noted as C XXX ( τ , ρ ), and the cross third-order cumulant of X ( n ) and Y ( n ) is noted as C XYX ( τ , ρ ) [20]:

C_{X X X} (τ, ρ) = c u m [S (n) + W_{1} (n), S (n + τ) + W_{1} (n + τ), S (n + ρ) + W_{1} (n + ρ)]

C_{X Y X} (τ, ρ) = c u m [S (n) + W_{1} (n), S (n + τ - τ_{0}) + W_{1} (n + τ), S (n + ρ) + W_{1} (n + ρ)]

…”

Section: Theorymentioning

confidence: 99%

A High-Resolution Demodulation Algorithm for FBG-FP Static-Strain Sensors Based on the Hilbert Transform and Cross Third-Order Cumulant

Huang

Zhen

Zhang

et al. 2015

Sensors

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Static strain can be detected by measuring a cross-correlation of reflection spectra from two fiber Bragg gratings (FBGs). However, the static-strain measurement resolution is limited by the dominant Gaussian noise source when using this traditional method. This paper presents a novel static-strain demodulation algorithm for FBG-based Fabry-Perot interferometers (FBG-FPs). The Hilbert transform is proposed for changing the Gaussian distribution of the two FBG-FPs’ reflection spectra, and a cross third-order cumulant is used to use the results of the Hilbert transform and get a group of noise-vanished signals which can be used to accurately calculate the wavelength difference of the two FBG-FPs. The benefit by these processes is that Gaussian noise in the spectra can be suppressed completely in theory and a higher resolution can be reached. In order to verify the precision and flexibility of this algorithm, a detailed theory model and a simulation analysis are given, and an experiment is implemented. As a result, a static-strain resolution of 0.9 nε under laboratory environment condition is achieved, showing a higher resolution than the traditional cross-correlation method.

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“…The third-order cumulant of X ( n ) is noted as C XXX ( τ , ρ ), and the cross third-order cumulant of X ( n ) and Y ( n ) is noted as C XYX ( τ , ρ ) [20]:

C_{X X X} (τ, ρ) = c u m [S (n) + W_{1} (n), S (n + τ) + W_{1} (n + τ), S (n + ρ) + W_{1} (n + ρ)]

C_{X Y X} (τ, ρ) = c u m [S (n) + W_{1} (n), S (n + τ - τ_{0}) + W_{1} (n + τ), S (n + ρ) + W_{1} (n + ρ)]

…”

Section: Theorymentioning

confidence: 99%

A High-Resolution Demodulation Algorithm for FBG-FP Static-Strain Sensors Based on the Hilbert Transform and Cross Third-Order Cumulant

Huang

Zhen

Zhang

et al. 2015

Sensors

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“…Next, define the instantaneous delay error as . Then, using the definition , from (5), we can write (6) Note that the two vectors, and , are mutually orthogonal at each index , i.e.,…”

Section: B Convergence Analysismentioning

confidence: 99%

New Adaptive Algorithm for Delay Estimation of Sinusoidal Signals

Chakraborty

Zheng

2007

IEEE Signal Process. Lett.

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Abstract-In this letter, we address the problem of adaptively estimating the time delay of a noisy sinusoid received at two spatially separated sensors. By choosing the sampling frequency equal to four times the signal frequency, a simple adaptive algorithm for direct delay estimation is derived. Algorithm convergence in mean and mean square error is proved. Computer simulations are also included to demonstrate the effectiveness of the proposed method.

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“…On the other hand, in the derivation of the variance, it is assumed that the signal-to-noise ratios of the two inputs are equal and the spectral density of is flat [13] [17] . This is not true in practical cases.…”

Section: Evaluation Of the Adaptive Time Delay Estimatementioning

confidence: 99%

Mobile instrument for intelligent leak detection and location on underground water supply pipelines

Wen

Xin³

et al. 2005

Smart Structures and Materials 2005: Smart Sensor Technology and Measurement Systems

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The paper discusses the instrumentation of leak detection and location on underground water supply pipelines based on adaptive algorithms. The equipment is designed to be self-contained and portably mobile with sensors, signal conditioning and data acquisition units and a laptop PC operating signal processing, information analysis and instrument control. The vibrational acoustic signals collected on tubular pipelines are used for pinpointing a leak or leaks in buried pipelines. Because of the complexity and the heavy corruption with ambient noises, it is essential to use an appropriate signal model and the scheme of analysis and synthesis in order to extract leak signatures and specify leak locations. In addition, the features of vibrational acoustic signals vary with the materials, the sizes and the inbuilt conditions of tubular pipes. It is difficult to pre-determine the knowledge of signals, such as the spectral knowledge. Here, an adaptive detection and estimation strategy is proposed based on LMS adaptive filtering and modified Wavelet denoising. The availability for leak detection and pinpointing estimation is automatically analyzed by the signal processing procedure without any prerequisite on signals and the procedure may adaptively find the way to get better estimate.

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Comparative study of five LMS-based adaptive time delay estimators

Cited by 30 publications

References 19 publications

A High-Resolution Demodulation Algorithm for FBG-FP Static-Strain Sensors Based on the Hilbert Transform and Cross Third-Order Cumulant

A High-Resolution Demodulation Algorithm for FBG-FP Static-Strain Sensors Based on the Hilbert Transform and Cross Third-Order Cumulant

New Adaptive Algorithm for Delay Estimation of Sinusoidal Signals

Mobile instrument for intelligent leak detection and location on underground water supply pipelines

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