Monte Carlo Global Search for Bayesian, GPS, Parameter Estimation

Matthew C. Bromberg and Ilir F. Progri

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 and providing dramatically improved performance over conventional matched filter techniques.
Published in:	Proceedings of the 17th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2004) September 21 - 24, 2004 Long Beach Convention Center Long Beach, CA
Pages:	54 - 60
Cite this article:	Bromberg, Matthew C., Progri, Ilir F., "Monte Carlo Global Search for Bayesian, GPS, Parameter Estimation," Proceedings of the 17th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2004), Long Beach, CA, September 2004, pp. 54-60.
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