Underwater Source Localization Using TDOA and FDOA Measurements with Unknown Propagation Speed and Sensor Parameter Errors

Journal article (2018)

Authors

Zhang Zhang National University of Defense Technology
Hu Hu Signal Processing Systems - EEMCS
Wang Wang National University of Defense Technology
Zhuang Zhuang National University of Defense Technology
Research Group
Signal Processing Systems (EEMCS) (TU Delft)
Copyright: © 2018 Bingbing Zhang, Y. Hu, Hongying Wang, Zhaowen Zhuang
DOI: https://doi.org/10.1109/ACCESS.2018.2852636
Uncertainty Measurement uncertainty Doppler effect Current measurement Sea measurements Covariance matrices Oceans Algebraic solution Frequency difference of arrival (FDOA) Sensor node uncertainty Sound propagation speed uncertainty Time difference of arrival (TDOA) Underwater localization
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Published Date

2018

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

Abstract

Underwater source localization problems are complicated and challenging: a) the sound propagation speed is often unknown and the unpredictable ocean current might lead to the uncertainties of sensor parameters (i.e. position and velocity); b) the underwater acoustic signal travels much slower than the radio one in terrestrial environments, thus resulting into a significantly severe Doppler effect; c) energy-efficient techniques are urgently required and hence in favour of the design with a low computational complexity. Considering these issues, we propose a simple and efficient underwater source localization approach based on time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements, which copes with unknown propagation speed and sensor parameter errors. The proposed method mitigates the impact of the Doppler effect for accurately inferring the source parameters (i.e. position and velocity). The Cram?r-Rao lower bounds (CRLBs) for this kind of localization are derived and, moreover, the analytical study shows that our method can yield the performance that is very close to the CRLB, particularly under small noise. The numerical results not only confirm the above conclusions but also show that our method outperforms other competing approaches.