A new estimator is proposed to extract the true distance between a mobile and a base sta- tion from a set of time-of-arrival (TOA) data, cor- rupted by unknown non-line-of-sight (NLOS) errors and Gaussian measurement noise. Characteristics of the estimator are discussed for a class of NLOS er- rors with variance considerably greater than that of the measurement noise, which is usually the case in practice. A quantization approach is used to estimate the probability density function of the observed TOA measurements and the TOA estimator is computed from this density estimate.
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