2010Unpublished venueRequires access

Research of time-delay estimation based on fourth-second order normalized cumulant

Hongzhi Wang, Jingtao Zhao, Liying Qian

Open publisher page 2 citations

Abstract

The problem of estimating the difference in arrival times of signals at two separated sensors is considered. The method based on fourth-second order normalized cumulants and Least Mean Square algorithm. In this paper, we study the normalized cumulants of the two different received signals by sensors and minimize the mean square error using Least Mean Square algorithm for time-delay estimation. Creating a new objective function in adaptive process, when the filter converges, the weighted coefficient is calculated, while the estimation of the time-delay value is also obtained. Simulation results demonstrate that, with the normalized cumulants, convergence curve performance via the proposed method is good at the rate of convergence.

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What this paper is about

The problem of estimating the difference in arrival times of signals at two separated sensors is considered. The method based on fourth-second order normalized cumulants and Least Mean Square algorithm. In this paper, we study the normalized cumulants of the two different received signals by sensors and minimize the mean square error using Least Mean Square algorithm for time-delay estimation. Creating a new objective function in adaptive process, when the filter converges, the weighted coefficient is calculated, while the estimation of the time-delay value is also obtained. Simulation results demonstrate that, with the normalized cumulants, convergence curve performance via the proposed method is good at the rate of convergence.

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OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

The problem of estimating the difference in arrival times of signals at two separated sensors is considered. The method based on fourth-second order normalized cumulants and Least Mean Square algorithm. In this paper, we study the normalized cumulants of the two different received signals by sensors and minimize the mean square error using Least Mean Square algorithm for time-delay estimation. Creating a new objective function in adaptive process, when the filter converges, the weighted coefficient is calculated, while the estimation of the time-delay value is also obtained. Simulation results demonstrate that, with the normalized cumulants, convergence curve performance via the proposed method is good at the rate of convergence.

Key concepts: Cumulant, Convergence (economics), Mathematics, Mean squared error, Estimation theory, Least mean squares filter, Statistics, Function (biology)

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