Outlier Detection Performance of a Modified Z-Score Method in Time-Series RSS Observation With Hybrid Scale Estimators

Abstract

The modified Z-score (mZ-score) method has been used to detect outliers in time series received signal strength (RSS) observations. Its performance is dependent on the scale estimator used, and each has advantages and disadvantages over the others. One approach to developing a scale estimator that combines the advantages of two or more scale estimators is through scale estimator hybridization. In this paper, the outlier detection performance of a mZ-score method with different hybridization approaches for Sn and median absolute deviation (MAD) scale estimators is determined and analysed. Three different hybrid scale estimators are identified, namely weighted, maximum, and average hybrid scale estimators. The performance of the mZ-score method using the three different hybrid scale estimators is determined using three experimentally generated and publicly available time-series RSS datasets. Based on the simulation results, the weighted hybrid scale estimator results in the best outlier detection performance amongst the three hybrid scale estimators. When compared to the mean-shift-based outlier detection (MOD) technique, the kmeans clustering-based technique, and the density-based spatial clustering (DBSCAN) technique, the mZscore method with the weighted hybrid scale estimator performs better with little or no false alarm and false negative detections.

Enhancing Fingerprint Localization Accuracy With Inverse Weight-Normalized Context Similarity Coefficient-Based Fingerprint Similarity Metric

“…Given two fingerprint vectors, 𝐟 𝑖 and 𝐟 𝑗 , containing N number RSS measurements as shown in ( 1) and ( 2), respectively: (1), 𝑟𝑠𝑠 𝑖 (2), … , 𝑟𝑠𝑠 𝑖 (𝑁 − 1), 𝑟𝑠𝑠 𝑖 (𝑁) ]…”

“…As the demand for accurate indoor positioning solutions grows, fingerprint-based indoor localization has emerged as a promising technique. Fingerprint-based localization is a type of localization technique that uses positiondependent signal parameters (PDSPs), such as received signal strength (RSS) or channel state information (CSI), obtained from spatially deployed wireless access points (APs), to determine the location of a target located within an indoor environment [1], [2]. The localization process of the fingerprint-based system is in two phases, namely the offline and online phases [2].…”