Associative Search Network for RSSI-based Target Localization in Unknown Environments

Abstract : Received Signal Strength Indicator (RSSI) is commonly considered and is very popular for target localization applications, since it does not require extra-circuitry and is always available on current devices. Unfortunately, target localizations based on RSSI are a ected with many issues, above all in indoor environments. In this paper, we focus on the pervasive localization of target objects in an unknown environment. In order to accomplish the localization task, we implement an Associative Search Network (ASN) on the robots and we deploy a real test-bed to evaluate the e ectiveness of the ASN for target localization. The ASN is based on the computation of weights, to "dictate" the correct direction of movement, closer to the target. Results show that RSSI through an ASN is e ective to localize a target, since there is an implicit mechanism of correction, deriving from the learning approach implemented in the ASN.
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Communication dans un congrès
International Conference on Ad Hoc Networks (AdHocNets), Sep 2015, San Remo, Italy
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https://hal.inria.fr/hal-01183287
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Valeria Loscri, Salvatore Guzzo Bonifacio, Nathalie Mitton, Simone Fiorenza. Associative Search Network for RSSI-based Target Localization in Unknown Environments. International Conference on Ad Hoc Networks (AdHocNets), Sep 2015, San Remo, Italy. 〈hal-01183287〉

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