Energy-efficient time-of-flight estimation in the presence of outliers: A machine learning approach

Alexander Apartsin, Leon N. Cooper, Nathan Intrator

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


The time-of-flight (ToF) estimation problem is common in sonar, ultrasound, radar, and other remote sensing applications. The conventional ToF maximum-likelihood estimator (MLE) exhibits a rapid deterioration in the accuracy when the signal-to-noise ratio (SNR) falls below a certain threshold. This threshold effect emerges mostly due to appearance of outliers associated with the side lobes in the autocorrelation function of a narrowband source signal. In our previous work, we have introduced a bank of unmatched filters and biased ToF estimators derived using these filters. These biased estimators form a feature vector for training a classifier which, subsequently, is used for reducing the bias and the variance parts induced by outliers in the mean-square error (MSE) of the MLE. In this paper, we extend the above method by introducing an adaptive scheme for controlling the number of measurements (pulses) required to achieve a desired accuracy. We show that using the information provided by a classifier, it is possible to achieve the estimation error of the MLE but by using significantly less number of pulses and thus energy on average.

Original languageEnglish
Article number6701351
Pages (from-to)1306-1313
Number of pages8
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Issue number4
StatePublished - Apr 2014
Externally publishedYes


  • Biosonar
  • fusion of estimates
  • sonar
  • threshold effect
  • time-of-flight (ToF) estimation


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