Dithering in Quantized RSS Based Localization
2015 (English)In: Proc. IEEE 6th Int. Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Institute of Electrical and Electronics Engineers (IEEE), 2015, 245-248 p.Conference paper (Refereed)
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-square error as compared to the conventional ML estimator without dithering.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2015. 245-248 p.
Dithering, maximum likelihood estimation, localization, quantized received signal strength
Control Engineering Signal Processing
IdentifiersURN: urn:nbn:se:liu:diva-123709DOI: 10.1109/CAMSAP.2015.7383782ISI: 000380473300066ISBN: 978-1-4799-1963-5OAI: oai:DiVA.org:liu-123709DiVA: diva2:892289
IEEE 6th Int. Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Cancun, Mexico, December 13-16, 2015