Markov Chain, Monte Carlo Global Search and Integration for Bayesian, GPS, Parameter Estimation

Ilir Progri, Matthew Bromberg, and Jinling Wang

Peer Reviewed

Abstract: Bayesian estimation techniques are applied to the problem of time and frequency offset estimation for Global Positioning System receivers. The estimation technique employs Markov Chain Monte Carlo (MCMC) to estimate unknown system parameters, utilizing a novel, multi-dimensional, Bayesian, global optimization strategy for initializing a Metropolis-Hastings proposal distribution. The technique enables the design of a high performance multi-user GPS receiver, capable of overcoming the near-far problem when the relative signal power is on the order of 5 dB (single antenna element) and 20 dB (4 antenna element array) and providing dramatically improved performance over conventional matched filter techniques against interference and jamming when the relative jammer and satellite signal power is on the order of 20 dB (4 antenna element array).
Published in: NAVIGATION, Journal of the Institute of Navigation, Volume 56, Number 3
Pages: 195 - 204
Cite this article: Progri, Ilir, Bromberg, Matthew, Wang, Jinling, "Markov Chain, Monte Carlo Global Search and Integration for Bayesian, GPS, Parameter Estimation", NAVIGATION, Journal of The Institute of Navigation, Vol. 56, No. 3, Fall 2009, pp. 195-204.
https://doi.org/10.1002/j.2161-4296.2009.tb01755.x
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