Curve and surface fitting with splines
Curve and surface fitting with splines
Wireless Location in CDMA Cellular Radio Systems
Wireless Location in CDMA Cellular Radio Systems
On some detection and estimation problems in heavy-tailed noise
Signal Processing - Signal processing with heavy-tailed models
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
A Robust Statistical Approach to Non-Line-of-Sightmitigation
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
Joint Bayesian model selection and estimation of noisy sinusoidsvia reversible jump MCMC
IEEE Transactions on Signal Processing
A Nonlinear -Estimation Approach to Robust Asynchronous Multiuser Detection in Non-Gaussian Noise
IEEE Transactions on Signal Processing
Least squares algorithms for time-of-arrival-based mobile location
IEEE Transactions on Signal Processing
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The problem of locating mobile sensors has received considerable attention, particularly in the field of wireless communications. It is well-known that the presence of non-line-of-sight (NLOS) errors in the geo-location problem leads to severe degradation in the localization performance. In this paper, we propose a robust Bayesian method to mitigate the NLOS errors in location estimation of a single moving sensor, whereby the localization is performed using time-of-arrival (TOA) measurements. This method is based on the Markov chain Monte Carlo (MCMC) approach. Numerical simulations results illustrate the promising results of our method in a mixed line-of-sight (LOS) and NLOS environment.