Mobile velocity and direction of movement estimation in NLOS multipath environment. Ben Rejeb, N., Bousnina, I., Ben Salah, M. B., & Samet, A. In 2015 23rd European Signal Processing Conference (EUSIPCO), pages 359-363, Aug, 2015.
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Paper doi abstract bibtex
Paper doi abstract bibtex In this paper, we propose a new method to jointly estimate the Mobile Velocity (MV) and the Direction of Movement (DM). We exploit the NLOS multipath environment with Uniform Linear Arrays (ULAs) at the receiver. We consider the Gaussian and the Laplacian angular distribution for the incoming angle of arrivals, for being the most used ones in the literature. The proposed method uses the magnitudes and the phase of the received signals Cross-Correlation Functions (CCFs). We take as a benchmark the Tow Rays (TR) approach for the MV estimate. Performance is assessed via Monte Carlo simulation. Using the Root Mean Square Error (RMSE) as a measure of performance, our new estimator performs well over wide MV and DM ranges and outperforms the TR one for the MV estimation.
@InProceedings{7362405,
author = {N. {Ben Rejeb} and I. Bousnina and M. B. {Ben Salah} and A. Samet},
booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
title = {Mobile velocity and direction of movement estimation in NLOS multipath environment},
year = {2015},
pages = {359-363},
abstract = {In this paper, we propose a new method to jointly estimate the Mobile Velocity (MV) and the Direction of Movement (DM). We exploit the NLOS multipath environment with Uniform Linear Arrays (ULAs) at the receiver. We consider the Gaussian and the Laplacian angular distribution for the incoming angle of arrivals, for being the most used ones in the literature. The proposed method uses the magnitudes and the phase of the received signals Cross-Correlation Functions (CCFs). We take as a benchmark the Tow Rays (TR) approach for the MV estimate. Performance is assessed via Monte Carlo simulation. Using the Root Mean Square Error (RMSE) as a measure of performance, our new estimator performs well over wide MV and DM ranges and outperforms the TR one for the MV estimation.},
keywords = {correlation methods;estimation theory;Gaussian distribution;Laplace equations;mean square error methods;mobile radio;Monte Carlo methods;multipath channels;mobile velocity;MV estimation;movement estimation;NLOS multipath environment;direction of movement;uniform linear arrays;ULA;Gaussian distribution;Laplacian angular distribution;angle of arrivals;received signal cross-correlation functions;CCF;tow ray approach;TR approach;Monte Carlo simulation;root mean square error;RMSE;Europe;Mobile communication;Monte Carlo methods;AWGN;Yttrium;Gold;mobile velocity;direction of movement;cross-correlation function;SIMO configuration;NLOS miltipath environment},
doi = {10.1109/EUSIPCO.2015.7362405},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570105091.pdf},
}Downloads: 0
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