Low Complexity Iterative Localization of Time-Misaligned Terminals in Cellular Networks

Abstract

Recently, iterative localization has arisen as a promising approach to localize a Mobile Station (MS) in a cellular system. The conventional geo-location is obtained in a two-step approach: propagation delays are estimated and then the multi-lateration is responsible for the determination of the user position, based on the estimated delays. Iterative localization iterates between the two conventional steps to progressively refine delay estimates based on the position estimate available from the previous iterations. This localization scheme was seen to provide appealing performances compared to the two-step approach. It also seems to be computationally attractive with respect to direct localization that estimates the position using the digitized received signals directly. However, the iterative localization solution developed in literature relies on a strict time synchronization between MS and Base Stations (BSs). Moreover, the computational complexity of the iterative approach is not thoroughly compared to two-step and optimal solutions. This paper therefore proposes a new iterative localization method able to operate in a cellular system with time-misaligned terminals. We show by means of a detailed complexity analysis that the iterative positioning algorithm is one order of magnitude less complex than direct localization. Simulation results prove that the achievable performance after a few iterations approaches the performance of the direct localization solution.

Self-Synchronization Based Localization of a Time-Misaligned Transmitter in Cellular Networks

“…We consider the same case scenario studied in [7] where the IPE was improved for localization of a time-misaligned transmitter. Notice that it is considered that all BSs are perfectly time synchronized.…”

“…Therefore, the IPE algorithm also outperforms the two-step approach. Furthermore, the IPE algorithm has been extended for different case scenarios like: time-misaligned transmitters [7], rich multipath [8], angle-of-arrival based localization and others [9], [10]. Nonetheless, the IPE approach presents two main shortcomings.…”

Iterative NDA Positioning Using Angle-of-Arrival Measurements for IoT Sensor Networks

“…The computational complexity can be expressed as the number of real multiplications required to estimate the sensor position. To simplify the discussion, the addition, subtraction and division as well as the exponential and real part operations are considered negligible compared to multiplication operations [22]. Three real multiplications are needed for a complex multiplication [30].…”

“…In the following, the computational complexity of the proposed positioning algorithms is studied and compared to the DPE method. The number of real multiplications to implement the DPE is computed in [22] and given by…”

“…Paper [21] develops an AOA-based method to detect NLOS propagation based on the statistical properties of channels. Paper [22] has recently assessed the effect of multipath and Obstructed Lineof-Sight (OLOS) propagation on the performance of the TOAbased iterative positioning algorithm. Accordingly, the most significant contributions of this paper can be summarized as follows:…”