## 1 Introduction  
Indoor wireless localization involves locating an indoor radio frequency (RF) transmitter using a position-dependent signal parameter (PDSP) obtained from the detected RF signal with a localization algorithm [1, 2, 3]. The received signal strength (RSS) is the most used PDSP for indoor localization because it can be derived from a variety of wireless technologies with comparable localization performance. The RSS represents the average power of the received RF signal and can be used with two different kinds of localization algorithms, namely fingerprinting and trilateration [3, 4]. The trilateration approach makes use of path-loss propagation models to establish a relationship between the RSS measurements obtained by the receiver and the actual separations between the RF transmitters and receiver [4]. The fingerprinting approach involves two phases. The first phase, known as the offline phase, involves the generation of a radio map using RSS measurements obtained from several RF transmissions using a single receiver at fixed reference locations. The second phase is referred to as the \"online phase\". It involves locating an indoor user by searching the radio map for the corresponding reference location to the indoor user's instantaneously acquired RSS measurements [3]. There are several wireless technologies used for indoor localization, but the most used are Wi-Fi, Bluetooth, Zigbee, and ultra-wideband [3, 5]. The localization accuracy of the indoor wireless localization system depends on several factors. One of these factors is the accuracy of the RSS measurement, which is affected by RSS fluctuation [6; 7]. RSS fluctuates due to the presence of walls, furniture, crowds, ambient and temporal conditions, and differences in RSS measuring device hardware and configurations [6; 8]. Several research papers [6; 7; 9; 20; 21] have proposed various techniques for dealing with RSS fluctuations. Taking multiple RSS observations for each reference location is recommended in most research works. The measurements are then smoothed using a mean-averaging filter, a Kalman filter, a moving average filter, or a median filter to produce a single RSS representative of the observation [6; 13; 19; 20; 21]. Other, less commonly used techniques are based on RSS measurement diversity. This diversity is achieved by using Wi-Fi or Bluetooth anchors with multiple transmitter antennae. Alternatively, in the case of BLE, it can be accomplished by using different broadcast channels (channels 37, 38, and 39), and then aggregating the RSS measurements to obtain a single RSS measurement [11; 22; 23]. Table 1 summarizes recent research on how to deal with RSS fluctuation. When there are outliers in the time-series RSS observation, most of the techniques presented in Table 1 are less effective. For instance, smoothing techniques such as exponential smoothing and moving averages can help reduce the variability in time-series RSS observations, making it easier to identify outliers. However, this technique is ineffective in reducing the impact of outliers on the observation in the presence of highly influential outliers. RSS outliers are RSS observations that appear to be significantly different from the rest of the time-series RSS observations. They have a disproportionately large impact on the RSS mean calculation. In this paper, a method to detect outliers in time-series RSS observations is proposed.