Time delay estimation using the LMS adaptive filter--Static behavior

Reed, F.; Feintuch, P.; Bershad, N.J.

doi:10.1109/tassp.1981.1163614

Cited by 195 publications

(59 citation statements)

References 14 publications

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“…;P i P (2) where e(k) = y(k) ; P P i=;P w i (k)x(k ;i) a n d w is a positive scalar that controls the convergence rate and stability of the updating rule. Using the interpolation formula 20], s(k ; D) c a n b e expressed as…”

Section: Adaptive Algorithms For Delay Estimationmentioning

confidence: 99%

“…The CTDE constrains the FIR lter coe cients to be samples of a sinc function and it simpli es the LMSTDE algorithm considerably because in this case only the peak weight, say, w L (k), needs to be adapted according to (2) in each iteration. The delay estimate of this method, denoted bŷ…”

Section: B Ctdementioning

confidence: 99%

“…The least squares lter coe cients, denoted by w o = fw o i g for ;P i P , a r e g i v en by w o = arg min w fJ w (w)g (25) where J w (w) = P N k=1 y(k) ; P P i=;P w i x(k ; i) 2 which approaches the CRLB for SNR 1. However, the variance is larger than the CRLB by approximately 3 dB at low SNR.…”

Section: Optimum Realizations and Comparison With The Crlbmentioning

confidence: 99%

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Comparative study of five LMS-based adaptive time delay estimators

Ching

2001

IEE Proc., Radar Sonar Navig.

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A n umber of adaptive algorithms for estimating time di erence between signals received at two spatially separated sensors have been proposed to model the delay using an FIR lter. Among them, there are the LMSTDE, CTDE, ETDE, SETDE and ETDGE, which are computationally e cient because of the LMS implementation. In this paper, these ve m e t h o d s are compared in terms of estimation accuracy and computational complexity. I t i s p r o ved that the LMSTDE and ETDGE attain identical performance for su ciently long lter lengths, although the ETDE and SETDE perform similarly to the ETDGE at high signal-to-noise ratio (SNR) and low SNR, respectively. The CTDE involves minimum computational load but it is the worst estimator in the presence of noise. In addition, optimum realizations of the LMSTDE as well as the ETDE and its variants are derived and their delay v ariances are compared with the Cram er-Rao lower bound. Simulation results show that ETDGE outperforms the other four methods for a wide range of lter lengths at di erent S N R s .

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Section: Adaptive Algorithms For Delay Estimationmentioning

confidence: 99%

Section: B Ctdementioning

confidence: 99%

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Comparative study of five LMS-based adaptive time delay estimators

Ching

2001

IEE Proc., Radar Sonar Navig.

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“…Besides this, the tracking ability of RLS is rather ad-hoc in nature using a forgetting factor and the algorithm complexity for L weights is   2 O L operations rather than for LMS [6]. Adaptive filters are proven successful in a wide range of signal processing applications such as noise-cancellation [7,8], adaptive arrays [9], time-delay estimation [10], echo cancellation [11] and channel equalization [12].…”

Section: Introductionmentioning

confidence: 99%

FIR System Identification Using Feedback

Moir¹

2013

JSIP

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This paper describes a new approach to finite-impulse (FIR) system identification. The method differs from the traditional stochastic approximation method as used in the traditional least-mean squares (LMS) family of algorithms, in which we use deconvolution as a means of separating the impulse-response from the system input data. The technique can be used as a substitute for ordinary LMS but it has the added advantages that can be used for constant input data (i.e. data which are not persistently exciting) and the stability limit is far simpler to calculate. Furthermore, the convergence properties are much faster than LMS under certain types of noise input.

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“…In [4], a finite impulse response (FIR) filter is used to model the time delay and its estimate is computed from interpolating filter coefficients. Alternatively, explicit delay adjustment [5]- [8] can be achieved by constraining the filter coefficients to be some functions of the time delay.…”

Section: Introductionmentioning

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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Time delay estimation using the LMS adaptive filter--Static behavior

Cited by 195 publications

References 14 publications

Comparative study of five LMS-based adaptive time delay estimators

Comparative study of five LMS-based adaptive time delay estimators

FIR System Identification Using Feedback

New Adaptive Algorithm for Delay Estimation of Sinusoidal Signals

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