Statistical Trilateration with Skew-t Distributed Errors in LTE Networks

Abstract

Localization accuracy of trilateration methods in Long Term Evolution (LTE) cellular networks, which are based on time-of-arrival (ToA), may be highly degraded due to multipath and non-line of sight (NLoS) conditions in urban and indoor environments. Multipath mitigation techniques usually involve a high computational burden and require wideband signals to be effective, which limit their adoption in certain low-cost and low-power mobile applications using narrow-band signals. As an alternative to these conventional techniques, this paper analyzes an Expectation Maximization (EM) localization algorithm that considers the skewness introduced by multipath in the LTE ranging error distribution. The EM algorithm is extensively studied with realistic emulated LTE signals of 1.4 MHz bandwidth. The EM method is compared to a standard Nonlinear Least Squares (NLS) algorithm under ideal simulated conditions and using realistic outdoor measurements from a laboratory testbed. The EM method outperforms the NLS method when the ranging errors in the training and test stages have similar distributions.