Received Signal Strength Indication (RSSI) is commonly used to provide distance estimates in range-based localization. In most cases, the localization systems use RSS at short range where the distance estimates are more reliable or use RSS alongside other techniques such as Time of Flight (ToF). This is so, since RSSI measurements have relatively high variance at long range and are strongly influenced by occlusions and interference in the deployment region of the Radio Frequency (RF) devices. This paper presents an overview of common filtering techniques that can be used to process RSSI readings in order to improve the accuracy of range computation from raw RSSI with minimal computational overhead. The range estimates computed from the filtered data are compared with expected values of the perturbed range/distance expressed in terms of the Cramer-Rao Lower Bound (CRLB) for RSS distance estimation. Results show that filtering can significantly improve the accuracy of range estimation, highlighting the pros and cons of the presented filtering methods at different range values.

A Comparison of RSSI Filtering Techniques for Range-based Localization

Moses Ayodele Koledoye;Daniele De Martini;Simone Rigoni;Tullio Facchinetti
2018

Abstract

Received Signal Strength Indication (RSSI) is commonly used to provide distance estimates in range-based localization. In most cases, the localization systems use RSS at short range where the distance estimates are more reliable or use RSS alongside other techniques such as Time of Flight (ToF). This is so, since RSSI measurements have relatively high variance at long range and are strongly influenced by occlusions and interference in the deployment region of the Radio Frequency (RF) devices. This paper presents an overview of common filtering techniques that can be used to process RSSI readings in order to improve the accuracy of range computation from raw RSSI with minimal computational overhead. The range estimates computed from the filtered data are compared with expected values of the perturbed range/distance expressed in terms of the Cramer-Rao Lower Bound (CRLB) for RSS distance estimation. Results show that filtering can significantly improve the accuracy of range estimation, highlighting the pros and cons of the presented filtering methods at different range values.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11571/1350636
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