Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Ultra-wideband communications: fundamentals and applications
Ultra-wideband communications: fundamentals and applications
Posterior Cramer-Rao bounds for discrete-time nonlinear filtering
IEEE Transactions on Signal Processing
Analysis of wireless geolocation in a non-line-of-sight environment
IEEE Transactions on Wireless Communications
An overview of the challenges and progress in meeting the E-911 requirement for location service
IEEE Communications Magazine
Overview of radiolocation in CDMA cellular systems
IEEE Communications Magazine
Indoor geolocation science and technology
IEEE Communications Magazine
An empirically based path loss model for wireless channels in suburban environments
IEEE Journal on Selected Areas in Communications
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This paper considers the wireless non-line-of-sight (NLoS) geolocation in mixed LoS/NLoS environments by using the information of time-of-arrival. We derive the Cramér-Rao bound (CRB) for a deterministic shadowing, the asymptotic CRB (ACRB) based on the statistical average of a random shadowing, a generalization of the modified CRB (MCRB) called a simplified Bayesian CRB (SBCRB), and the Bayesian CRB (BCRB) when the a priori knowledge of the shadowing probability density function is available. In the deterministic case, numerical examples show that for the effective bandwidth in the order of kHz, the CRB almost does not change with the additional length of the NLoS path except for a small interval of the length, in which the CRB changes dramatically. For the effective bandwidth in the order of MHz, the CRB decreases monotonously with the additional length of the NLoS path and finally converges to a constant as the additional length of the NLoS path approaches the infinity. In the random shadowing scenario, the shadowing exponent is modeled by ζ=uσ where u is a Gaussian random variable with zero mean and unit variance and σ is another Gaussian random variable with mean µσ and standard deviation σσ. When µσ is large, the ACRB considerably increases with σσ, whereas the SBCRB gradually decreases with σσ. In addition, the SBCRB can well approximate the BCRB.