# MS Location Estimation Based on the Artificial Bee Colony Algorithm
## 1 Introduction  
With the rapid development of communication technology, the ability of searching for a mobile station (MS) location has become increasingly important in recent years. High-accuracy positioning can provide many commercial services for the users of wireless devices. Location-based services (LBSs) are used for car tracking, map navigation, and security applications. Among these, E-911, an emergency provision system regulated by the U.S. Federal Communications Commission (FCC), is one of the most well-known localization applications [1]. Generally, the propagation time can be measured by the MS and the base station (BS) in wireless cellular systems. Kinds of measurements are taken by the signals that travel between the MS and the BSs. According to different measurements, different known wireless location techniques are applied. Some of the most common schemes are the signal strength [2], angle of arrival (AOA) [3], time of arrival (TOA) [4], and time difference of arrival (TDOA) [5]. In order to provide better accuracy, hybrid TOA and AOA methods were proposed [6]. In [7], the authors utilized the improved linear-line-of-position to propose a TOA-based localization method for a 3-D environment. The accuracy of MS location estimation is affected by many factors, such as multipath, hearability, and non-line-of-sight (NLOS) propagation. One of the primary tasks for accurate location estimates in wireless communicationlocation systems is NLOS propagation derived from the blocking of direct paths. NLOS often happens in an urban environment and can be considered as a dominant issue for time-based location estimation. The additional propagation time or distance may lead to significant degradations on location performance. In an ideal case, if the line-of-sight (LOS) propagation exists, the MS location can be derived successfully without error interference. However, an LOS propagation channel is hard to come by in a real environment. To meet the high location accuracy, many location estimation algorithms were proposed to mitigate the NLOS error. To handle the problem of NLOS error, an omnidirectional mobile target-node (TN) localization technique with TOA and AOA measurements found by the antenna arrays was proposed, and the NLOS TN localization was theoretically derived based on the equations for NLOS identification [8]. In [9], the authors present an algorithm to classify the former into LOS and NLOS links by applying the propagation delay (PD) range estimations. A particle filter localization algorithm was proposed regarding the model for accounting NLOS conditions, and this method is feasible to simplify the working assumptions [10]. In [11], the authors proposed a non-iterative hybrid least square estimator to appraise the mobile unit (MU) coordinates using the TDOA measurements and direction of arrival estimates. For the reasons of NLOS interference reduction, the authors of this paper proposed several geometrical location methods by applying TOA measurements and AOA measurements simultaneously [12; 13]. The authors put forward a method that combines the hybrid TOA/AOA measurements and short-range TOA measurements to appraise the location [14]. A model is proposed to simulate the situation that MS was in the NLOS transmission environment, whereas BSs have some links with the MS in an LOS environment. In recent years, a lot of algorithms have been widely employed to resolve various constrained optimization problems. Optimization techniques can be categorized into two types, evolutionary algorithms (EAs) and swarm intelligence-based algorithms. The genetic algorithm, designated as following the evolution procedures in nature, is one of the most famous optimization methods among EAs. The whale optimization algorithm was proposed in [15], and its application on mobile positioning has been published. The authors in [16] used the distance measurements to derive the overlap of all the base stations and find out the unknown mobile station in the overlap by WOA.