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The mobile device localization has been an area of great interest for researchers, network operators and vendors in recent times. Mobile device localization means determining the coordinates of a mobile terminal in two dimensions (latitude and longitude) or three dimensions (latitude, longitude, and altitude). This indeed became significant due to the curiosity of the Federal Communication Commission (FCC) in demand for an accurate radio device localization method, and also its application in modern day’s location based services such as fleet management, navigation systems, billing, access control, security, monitoring, and among others (Bussgang, 1998; Sayed et al., 2005; Yassin et al., 2016; Lee et al., 2017; Ardakani et al., 2020; Ezema & Ani, 2020; Aernouts, 2020). The FCC is a communication regulatory body set up by the United States of America (USA) government. As rapid response to emergency situations is one of its targets, this body has introduced emergency numbers such as E911 and E112. The E911 and E112 are service numbers provided to mobile users. It means that any mobile user that dialed any of the aforementioned numbers should be located at once. The FCC has released the phase III performance benchmark in terms of location accuracy for emergency services. The benchmark as recommended by FCC in terms of percentage position error is 100m and 300m at 67%, and 95%, respectively (Ezema & Ani, 2017, 2020). The enforcement of this standard on mobile operators in USA is to take effect by the year 2021 (Ezema & Ani, 2017). To that effect, regulatory bodies, vendors and operators are on the toe of researchers in coming up with an approach that could lead to a better radiolocation estimation.
Many practical localization parameters have been employed to estimate mobile position. In general, these parameters can be grouped into three categories namely; time-based, Angle of Arrival (AoA) based and Received Signal Strength based (Yassin et al., 2016; Ardakani et al., 2020; Janssen et al., 2020). The use of AoA requires not less than two angles and two reference points for location estimates. The ToA utilizes the time it takes the signal to travel from the transmitter to the receiver. The TDoA uses the travel time difference from each receiver to estimate the mobile user position while RSS uses the intensity of signals to achieve localization estimates (Yassin et al., 2016; Aernouts, 2020).
Each of these localization parameters, when used for location estimates are prone to error. The magnitude of the error can be attributed to the effects of various factors such as; non line of sight (NLOS) conditions, multipath environment, deep shadowing effects, signal propagation, and synchronization related problems (Nwawelu et al., 2012; Yassin et al., 2016; Brena et al., 2017; Ardakani et al., 2020). If there are errors when AoA is used then it is as a result of NLOS scenario. The NLOS exist when a signal cannot avoid obstruction. This situation emanates when there is no clear trajectory between the transmitter and receiver. It should be noted that both ToA and TDoA are chiefly dependent on time. In ToA and TDoA, synchronization are done at the transmitter and receiver, respectively. Improper synchronization either at the receiver or transmitter when any of the time dependent location parameters are employed leads to errors. The strength of signal when being propagated attenuates gradually as the distance from the source increases. Also, the strength of signal can be impaired due to reflection, scattering and interferences (Nwawelu et al., 2012).