A Deep Neural Network Approach for Time-Of- Arrival Estimation in Multipath Channels

Oded Bialer; Noa Garnett; Dan Levi

ICASSP 2018accepted0 citations

A Deep Neural Network Approach for Time-Of- Arrival Estimation in Multipath Channels

Abstract

Attaining accurate estimation of a signal time-of-arrival (TOA) in dense multipath channels is very challenging. This problem was traditionally solved with signal processing techniques. In this paper, a novel deep convolutional neural networks (DCNN) TOA estimator is developed. The DCNN was trained with synthetically generates multipath channel realizations based on statistical modeling, and then tested on real-life measurements from indoor environments. It is shown that the DCNN attains the performance of state-of-the-art signal processing based estimators (maximum likelihood performance), and has the advantage that it does not require knowledge of the channel statistics nor the knowledge of the transmitted signal waveform. This work inspires further study on the applicability of neural networks to other related problems, which have been traditionally solved with signal processing methods.

BibTeX

@inproceedings{icassp2018_adeepneuralnetwo,
  title = {A Deep Neural Network Approach for Time-Of- Arrival Estimation in Multipath Channels},
  author = {Oded Bialer and Noa Garnett and Dan Levi},
  booktitle = {ICASSP 2018},
  year = {2018}
}