## 1 Introduction  
A precise real-time indoor positioning system has recently attracted considerable attention from researchers due to location-aware services, which include patient location monitoring, mobile advertising, tag tracking, and robot guiding [1; 2; 3; 4; 5]. Unlike Global Positioning Systems (GPS), that require line of sight (LOS) transmission paths, indoor positioning faces many challenges due to changeable radio propagation environments inside the buildings [6]. Due to walls, cells, movement of people or furniture, and obstacles inside the buildings, indoor radio propagation is affected by multipath fading, shadowing, and delay distortion [7]. In addition to high-accuracy requirements, indoor position systems should also estimate the position of an object quickly with a light algorithm and low computational cost. Many indoor positioning techniques have been promoted such as Wireless local Area Network (WLAN), Ultrasound, Lighting, Radio Frequency Identification (RFID), and Bluetooth. WLAN-based indoor positioning systems have been widely employed for IPS without additional cost because WLAN infrastructures are necessary in large buildings like airports, hospitals, universities, and museums. Positioning based on RSS can be divided into two models: signal propagation models, and fingerprint-based location models. The requirements of the first model for indoor propagation loss is relatively high, so this article focuses on the second model. Although the second model has been relatively mature, the WLAN-based fingerprinting technique can be considered as an important technique used to estimate the position of a target. In general, fingerprinting-based positioning involves two phases: an offline and an online phase [8]. In the offline phase, the area of interest is divided into grid points, which are called reference points (RPs). Each RP\({}_{i}\) is labeled by Cartesian coordinates (\(x_{i}\),\(y_{i}\)). The MS at each RP\({}_{i}\) collects the RSS readings from various APs, which are deployed in the Area of Interest (AOI). In the online phase, the MS records the RSS vector at an unknown location and pre-matches with the stored RM to determine the closest RPs to the MS by using Euclidian distance, which is the simplest method to estimate the position of MS.  
Fortunately, Wi-Fi users are able to measure the RSS from various APs, which are deployed in the AOI. Therefore, location fingerprints are typically vectors of RSS of different APs, and the length of such vectors can be grown to include all the detectable APs in the AOI. Unfortunately, not all these APs can contribute positively to positioning, the majority of them are redundant. Therefore, including all these APs in the RSS vector results in superfluous computational costs, and in some cases, causes a deterioration in the accuracy [9]. Recently, most APs that are deployed in large buildings, use Multiple Service Set Identifier configurations, which means that each AP is configured to transmit N signals instead of one signal. MSSID is used in most buildings to advertise different WLANs where each WLAN has a different class of clients, and each class requires different keys, firewalls, privacy, and speeds [10]. This technique leads to increasing redundancy in the calculation process and a decrease in the accuracy of systems when each SSID is considered as a single AP [11]. In addition to the redundancy of APs, the accuracy of fingerprinting-based techniques is severely affected by the multipath issue and radio interference. Therefore, it is very important for any IPS designer to know the statistical properties of RSSI signals. Fluctuation in RSS signals due to the multipath phenomenon is an inherent issue in fingerprinting-based techniques. There are many solutions to mitigate the effect of a multipath issue in offline and online phases including the use of Kalman filter, averaging, and histograms [12; 13; 14].