In this work we attack the problem of mobile terminal (MT) location estimation, using the wireless network's infrastructure. This is a cumbersome but at the same time intriguing problem. Cumbersome because the conditions encountered in wireless propagation environments, namely multipath and non-line-of-sight (NLoS), can lead to serious degradation of the performance of traditional localization methods. Intriguing because one could come up with fundamentally different methods to tackle the difficulties and utilize mathematical tools from the wider area of statistical signal processing, to analyze their performance. Moreover, it is an interesting topic due to the numerous applications that can utilize the MT location information, e.g. emergency call detection, location-sensitive billing, increased data rate due to optimum resource allocation, navigational and yellow-pages services.
Applications of statistical signal processing in mobile terminal localization
Traditional localization methods are 2-step processes: In the first step a set of location-dependent parameters (LDP) is estimated. In the second step, the MT location is estimated by finding the position that best fits the LDP estimates. Although we mainly focused on the second step of localization, to complement our methods, we have developed a high-resolution low-complexity LDP estimation algorithm for MIMO-OFDM systems. The algorithm, which is commonly known as 4D Unitary ESPRIT, exploits the rotation invariance structure of the time-frequency channel transfer function to estimate accurately the angles of arrival (AoA), the angles of departure (AoD), the times of arrival (ToA) and the Doppler shifts (DS) of the multipath components (MPC).
As far as the second step of localization is concerned, we developed several hybrid methods applicable to NLoS environments. The motive behind this choice is two-fold: On one hand hybrid methods can be used to localize the MT with just 1 base station, if the richness of the channel is exploited. Therefore, ``hearability'' is not an issue anymore. On the other hand, a LoS component rarely exists in eg. indoor and dense urban environments. Besides, for the case when a LoS component does exist, localization has been studied extensively over the past decades and many good solutions have been proposed.
The main reason for which the NLoS localization problem is still an open topic, is the difficulty in mapping the LDP estimates to a unique location. To this end, in most of our methods, we utilize the single-bounce model (SBM) or simple variations of it, like the Dynamic-SBM, which is the result of integrating the SBM with a mobility model. The 2 great advantages of the SBM-based methods are the identifiability even for cases when LDP estimates are available for only 2 MPC and the remarkable performance for cases when the channel is richer. Due to these great advantages, we consider SBM-based methods to be an appealing solution for the NLoS localization problem and thus we studied them thoroughly. Specifically, we investigated identifiability for different scenarios, we analyzed the impact of both the LDP accuracy and the geometric configuration on performance, we derived closed-form solutions for the case when all 4 aforementioned LDP are available and for the more interesting case when AoA can not be accurately estimated.
Last but not least, we have developed a direct location estimation (DLE) method for MIMO-OFDM systems that operate in NLoS environments. In contrast to traditional methods, DLE combines the 2 steps of localization into a single one and thus can estimate the MT location directly from the received signal. The main advantage of doing so is the enhanced accuracy at low to medium signal-to-noise ratios (SNR) and/or with small number of data samples, as demonstrated by our results.
Systèmes de Communication
© TELECOM ParisTech. Personal use of this material is permitted. The definitive version of this paper was published in Thesis and is available at :
PERMALINK : https://www.eurecom.fr/publication/3171