Research Article
Associative Search Network for RSSI-Based Target Localization in Unknown Environments
@INPROCEEDINGS{10.1007/978-3-319-25067-0_12, author={V. Loscr\^{\i} and S. Bonifacio and N. Mitton and S. Fiorenza}, title={Associative Search Network for RSSI-Based Target Localization in Unknown Environments}, proceedings={Ad Hoc Networks. 7th International Conference, AdHocHets 2015, San Remo, Italy, September 1--2, 2015, Proceedings}, proceedings_a={ADHOCNETS}, year={2015}, month={9}, keywords={ASN Target Localization RSSI robots experimentation}, doi={10.1007/978-3-319-25067-0_12} }- V. Loscrí
S. Bonifacio
N. Mitton
S. Fiorenza
Year: 2015
Associative Search Network for RSSI-Based Target Localization in Unknown Environments
ADHOCNETS
Springer
DOI: 10.1007/978-3-319-25067-0_12
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 affected 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 effectiveness 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 effective to localize a target, since there is an implicit mechanism of correction, deriving from the learning ASN approach.