Outlier Detection in Time-Series Receive Signal Strength Observation Using Z-Score Method with <inline-formula><math display="inline"><semantics><mrow><msub><mrow><mi>S</mi></mrow><mrow><mi>n</mi></mrow></msub></mrow></semantics></math></inline-formula> Scale Estimator for Indoor Localization

Abstract

Collecting time-series receive signal strength (RSS) observations and averaging them is a common method for dealing with RSS fluctuation. However, outliers in the time-series observations affect the averaging process, making this method less efficient. The Z-score method based on the median absolute deviation (MAD) scale estimator has been used to detect outliers, but it is only efficient with symmetrically distributed observations. Experimental analysis has shown that time-series RSS observations can have a symmetric or asymmetric distribution depending on the nature of the environment in which the measurement was taken. Hence, the use of the Z-score method with the MAD scale estimator will not be efficient. In this paper, the Sn scale estimator is proposed as an alternative to MAD to be used with the Z-score method in detecting outliers in time-series RSS observations. Performance comparison using an online RSS dataset shows that the Z-score with MAD and Sn as scale estimators falsely detected about 50% and 13%, respectively, of the RSS observations as outliers. Furthermore, the average absolute RSS median deviations between raw and outlier-free observations are 3 dB and 0.25 dB, respectively, for the MAD and Sn scale estimators, corresponding to a range error of about 2 m and 0.5 m.

Keywords

• RSS fluctuation
• MAD
• <i>S_n</i> scale-estimator
• outlier