Minimizing false peak errors in generalized cross-correlation time delay estimation using subsample time delay estimation

Soo Hwan Choi, Doo-Seop Eom

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The Generalized cross-correlation (GCC) method is most commonly used for time delay estimation (TDE). However, the GCC method can result in false peak errors (FPEs) especially at a low signal to noise ratio (SNR). These FPEs significantly degrade TDE, since the estimation error, which is the difference between a true time delay and an estimated time delay, is larger than at least one sampling period. This paper introduces an algorithm that estimates two peaks for two cross-correlation functions using three types of signals such as a reference signal, a delayed signal, and a delayed signal with an additional time delay of half a sampling period. A peak selection algorithm is also proposed in order to identify which peak is closer to the true time delay using subsample TDE methods. This paper presents simulations that compare the algorithms' performance for varying amounts of noise and delay. The proposed algorithms can be seen to display better performance, in terms of the probability of the integer TDE errors, as well as the mean and standard deviation of absolute values of the time delay estimation errors.

Original languageEnglish
Pages (from-to)304-311
Number of pages8
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE96-A
Issue number1
DOIs
Publication statusPublished - 2013 Jan 1

Fingerprint

Time Delay Estimation
Cross-correlation
Time delay
Time Delay
Estimation Error
Error analysis
Cross-correlation Function
Correlation methods
Mean deviation
Absolute value
Standard deviation
Sampling
False
Integer
Estimate
Signal to noise ratio
Simulation

Keywords

  • Cross-correlaion
  • False peak error
  • Subsample delay estimation
  • Time delay estimation

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Graphics and Computer-Aided Design
  • Applied Mathematics
  • Signal Processing

Cite this

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abstract = "The Generalized cross-correlation (GCC) method is most commonly used for time delay estimation (TDE). However, the GCC method can result in false peak errors (FPEs) especially at a low signal to noise ratio (SNR). These FPEs significantly degrade TDE, since the estimation error, which is the difference between a true time delay and an estimated time delay, is larger than at least one sampling period. This paper introduces an algorithm that estimates two peaks for two cross-correlation functions using three types of signals such as a reference signal, a delayed signal, and a delayed signal with an additional time delay of half a sampling period. A peak selection algorithm is also proposed in order to identify which peak is closer to the true time delay using subsample TDE methods. This paper presents simulations that compare the algorithms' performance for varying amounts of noise and delay. The proposed algorithms can be seen to display better performance, in terms of the probability of the integer TDE errors, as well as the mean and standard deviation of absolute values of the time delay estimation errors.",
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N2 - The Generalized cross-correlation (GCC) method is most commonly used for time delay estimation (TDE). However, the GCC method can result in false peak errors (FPEs) especially at a low signal to noise ratio (SNR). These FPEs significantly degrade TDE, since the estimation error, which is the difference between a true time delay and an estimated time delay, is larger than at least one sampling period. This paper introduces an algorithm that estimates two peaks for two cross-correlation functions using three types of signals such as a reference signal, a delayed signal, and a delayed signal with an additional time delay of half a sampling period. A peak selection algorithm is also proposed in order to identify which peak is closer to the true time delay using subsample TDE methods. This paper presents simulations that compare the algorithms' performance for varying amounts of noise and delay. The proposed algorithms can be seen to display better performance, in terms of the probability of the integer TDE errors, as well as the mean and standard deviation of absolute values of the time delay estimation errors.

AB - The Generalized cross-correlation (GCC) method is most commonly used for time delay estimation (TDE). However, the GCC method can result in false peak errors (FPEs) especially at a low signal to noise ratio (SNR). These FPEs significantly degrade TDE, since the estimation error, which is the difference between a true time delay and an estimated time delay, is larger than at least one sampling period. This paper introduces an algorithm that estimates two peaks for two cross-correlation functions using three types of signals such as a reference signal, a delayed signal, and a delayed signal with an additional time delay of half a sampling period. A peak selection algorithm is also proposed in order to identify which peak is closer to the true time delay using subsample TDE methods. This paper presents simulations that compare the algorithms' performance for varying amounts of noise and delay. The proposed algorithms can be seen to display better performance, in terms of the probability of the integer TDE errors, as well as the mean and standard deviation of absolute values of the time delay estimation errors.

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