Performance Evaluation of Robust Scale Estimators for Modified Z-Score-Based Outlier Detection in Received Signal Strength-Based Time-Series Observations

Abstract

In fingerprinting-based localization systems, collecting time-series received signal strength (RSS) observations and averaging them is a common method for dealing with RSS fluctuation during the creation of the fingerprint database. However, in practice, these observations contain outliers caused by multipath propagation, interference, and hardware instability, which degrade the effectiveness of the averaging process. The modified Z-score method is widely adopted for outlier detection due to its simplicity and moderate accuracy. However, its performance depends strongly on the choice of scale estimator used to quantify data dispersion. A systematic evaluation of robust scale estimators under varying RSS observation sizes, with emphasis on detection reliability and computational cost, remains limited. This study evaluates six robust scale estimators within the modified Z-score framework, namely median absolute deviation (MAD), Sn, Qn, interquartile range (IQR), trimmed standard deviation (TSD), and Winsorized standard deviation (WSD). The goal is to identify a scale estimator that ensures reliable outlier detection while maintaining low to moderate computational cost for practical deployment in fingerprinting systems. Results show that, in terms of outlier detection performance, the IQR and Sn estimators achieve the lowest false detection rates across different observation sizes. In terms of computational efficiency, the MAD estimator provides the lowest processing time based on empirical runtime measurements. When both detection performance and computational efficiency are considered, the IQR estimator offers the most practical solution for preprocessing RSS time-series observations in fingerprint database creation for fingerprinting-based systems.