Efficient cooperative localization algorithm in LOS/NLOS environments. Jin, D., Yin, F., Fritsche, C., Zoubir, A. M., & Gustafsson, F. In 2015 23rd European Signal Processing Conference (EUSIPCO), pages 185-189, Aug, 2015. Paper doi abstract bibtex The well-known cooperative localization algorithm, `sum-product algorithm over a wireless network' (SPAWN) has two major shortcomings, a relatively high computational complexity and a large communication load. Using the Gaussian mixture model with a model selection criterion and the sigma-point (SP) methods, we propose the SPAWN-SP to overcome these problems. The SPAWN-SP easily accommodates different localization scenarios due to its high flexibility in message representation. Furthermore, harsh LOS/NLOS environments are considered for the evaluation of cooperative localization algorithms. Our simulation results indicate that the proposed SPAWN-SP demonstrates high localization accuracy in different localization scenarios, thanks to its high flexibility in message representation.
@InProceedings{7362370,
author = {D. Jin and F. Yin and C. Fritsche and A. M. Zoubir and F. Gustafsson},
booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
title = {Efficient cooperative localization algorithm in LOS/NLOS environments},
year = {2015},
pages = {185-189},
abstract = {The well-known cooperative localization algorithm, `sum-product algorithm over a wireless network' (SPAWN) has two major shortcomings, a relatively high computational complexity and a large communication load. Using the Gaussian mixture model with a model selection criterion and the sigma-point (SP) methods, we propose the SPAWN-SP to overcome these problems. The SPAWN-SP easily accommodates different localization scenarios due to its high flexibility in message representation. Furthermore, harsh LOS/NLOS environments are considered for the evaluation of cooperative localization algorithms. Our simulation results indicate that the proposed SPAWN-SP demonstrates high localization accuracy in different localization scenarios, thanks to its high flexibility in message representation.},
keywords = {computational complexity;cooperative communication;Gaussian processes;mixture models;wireless sensor networks;message representation;SP method;sigma-point method;model selection criterion;Gaussian mixture model;computational complexity;SPAWN;wireless network;sum product algorithm;LOS-NLOS environment;cooperative localization algorithm;Approximation methods;Signal processing algorithms;Complexity theory;Signal processing;Indexes;Parametric statistics;Europe;Cooperative localization;SPAWN;low-complexity;sigma-point methods},
doi = {10.1109/EUSIPCO.2015.7362370},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570097329.pdf},
}
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