Abstract
Mobile station (MS) localization in a cellular network is appealing to both industrial community and academia, due to the wide applications of location-based services. The main challenge is the unknown one-bound (OB) and multiple-bound (MB) scattering environment in dense multipath environment. Moreover, multiple base stations (BSs) are required to be involved in the localization process, and the precise time synchronization between MS and BSs is assumed. In order to address these problems, hybrid time of arrival (TOA), angle of departure (AOD), and angle of arrival (AOA) measurement model from the serving BS with the synchronization error is investigated in this paper. In OB scattering environment, four linear least square (LLS), one quadratic programming and data fusion-based localization algorithms are proposed to eliminate the effect of the synchronization error. In addition, the Cramer-Rao lower bound (CRLB) of our localization model on the root mean-square error (RMSE) is derived. In hybrid OB and MB scattering environment, a novel double identification algorithm (DIA) is proposed to identify the MB path. Simulation results demonstrate that the proposed algorithms are capable to deal with the synchronization error, and LLS-based localization algorithms show better localization accuracy. Furthermore, the DIA can correctly identify the MB path, and the RMSE comparison of different algorithms further prove the effectiveness of the DIA.
Highlights
The ability to accurately determine the location of mobile station (MS) in a cellular network is a vital component of numerous applications, such as emergency services, commercial service and network optimization [1]
In [19], the authors analyze the performance of an Millimeter wave (mmWave) localization approach that can utilize time of arrival (TOA)/angle of departure (AOD)/angle of arrival (AOA) measurements in an urban environment with both line of sight (LOS) and OB scattering, and proposes gradient-assisted particle filter method to accurately estimate the location of MS as well as nearby scatterers with radio-environmental mapping
In order to deal with the synchronization error, four linear least square (LLS), a quadratic programming (QP) and data fusion (DF) based localization algorithms are proposed in this paper
Summary
The ability to accurately determine the location of mobile station (MS) in a cellular network is a vital component of numerous applications, such as emergency services, commercial service and network optimization [1]. Based on the linearization of TOA/AOD/AOA measurements with a first order Taylor series, a three-dimensional localization algorithm is proposed in [14] through utilizing the geometry relationship of the OB scattering paths. In [19], the authors analyze the performance of an mmWave localization approach that can utilize TOA/AOD/AOA measurements in an urban environment with both line of sight (LOS) and OB scattering, and proposes gradient-assisted particle filter method to accurately estimate the location of MS as well as nearby scatterers with radio-environmental mapping. Since the variables in (3) are independent, a new least square (LS) algorithm denoted as LLS is utilized to obtain the estimated location of MS and the synchronization error. Based on the above two considerations, a novel double identification algorithm (DIA) is proposed, and it proceeds as three steps:
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