Dithering in quantized RSS based localization

Di Jin, Abdelhak M. Zoubir, Feng Yin, Carsten Fritsche, Fredrik Gustafsson
2015 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)  
We study maximum likelihood (ML) position estimation using quantized received signal strength measurements. In order to mitigate the undesired quantization effect in the observations, the dithering technique is adopted. Various dither noise distributions are considered and the corresponding likelihood functions are derived. Simulation results show that the proposed ML estimator with dithering is able to generate a significantly reduced bias but a modestly increased mean-squareerror as compared to the conventional ML estimator without dithering.
doi:10.1109/camsap.2015.7383782 dblp:conf/camsap/JinZYFG15 fatcat:3x7f4jghgbfadbt45fhvrysgqm