Compressed PARAFAC Model-based Two-Dimensional Angle Estimation for Acoustic Vector-Sensor Arrays

DOI

10.2991/mecs-17.2017.141How to use a DOI?

Keywords

arbitrary array, acoustic vector-sensor, compress, PARAFAC model, angle estimation.

Abstract

In this paper, in order to estimate the angles for arbitrarily spaced arrays with acoustic vector-sensor, we combine the compressed sensing theory with parallel factor (PARAFAC) model, and propose a neoteric angle estimation algorithm. The proposed algorithm firstly compressed the PARAFAC model, then exploit trilinear alternating least square (TALS) algorithm to estimate the parameter matrices and obtains the angle estimation. Owing to compression, the proposed algorithm has smaller storage requirement and lower computational complexity, compared with the conventional PARAFAC algorithm. It's also works well to achieve automatically paired azimuth and elevation angles. The angle estimation performance of the proposed algorithm is close to the conventional PARAFAC algorithm, and is better than the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. Various simulation results demonstrate the effectiveness of our algorithm.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Le Xu
AU  - You Sun
AU  - Na Shi
AU  - Xiaofei Zhang
PY  - 2016/06
DA  - 2016/06
TI  - Compressed PARAFAC Model-based Two-Dimensional Angle Estimation for Acoustic Vector-Sensor Arrays
BT  - Proceedings of the 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)
PB  - Atlantis Press
SP  - 232
EP  - 237
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecs-17.2017.141
DO  - 10.2991/mecs-17.2017.141
ID  - Xu2016/06
ER  -