TY - JOUR
T1 - A robust localization algorithm for mobile sensors using belief functions
AU - Mourad, Farah
AU - Snoussi, Hichem
AU - Abdallah, Fahed
AU - Richard, Cédric
PY - 2011/5
Y1 - 2011/5
N2 - One of the main objectives of localization algorithms is to compute accurate estimates of sensor positions. This task is usually performed using measurements exchanged with neighboring sensors. However, when erroneous measurements occur, the localization process may yield wrong estimates, which leads to unreliable information for location-based applications. This paper proposes a robust localization technique that works efficiently, even under unreliable measurements assumptions. The proposed method uses belief function theory to estimate sensors locations. Assuming that the reliability of sensors measurements is known, the method combines all the available information to make a final decision about the positions. Each measurement is then used to define a belief function based on the reliability information. Experiments with simulated data demonstrate the effectiveness of this approach compared with state-of-the-art methods using different combination rules.
AB - One of the main objectives of localization algorithms is to compute accurate estimates of sensor positions. This task is usually performed using measurements exchanged with neighboring sensors. However, when erroneous measurements occur, the localization process may yield wrong estimates, which leads to unreliable information for location-based applications. This paper proposes a robust localization technique that works efficiently, even under unreliable measurements assumptions. The proposed method uses belief function theory to estimate sensors locations. Assuming that the reliability of sensors measurements is known, the method combines all the available information to make a final decision about the positions. Each measurement is then used to define a belief function based on the reliability information. Experiments with simulated data demonstrate the effectiveness of this approach compared with state-of-the-art methods using different combination rules.
KW - Belief functions
KW - connectivity measurements
KW - distributed estimation
KW - intervals
KW - reliability of sensors
UR - http://www.scopus.com/inward/record.url?scp=79955952040&partnerID=8YFLogxK
U2 - 10.1109/TVT.2011.2115265
DO - 10.1109/TVT.2011.2115265
M3 - Article
AN - SCOPUS:79955952040
SN - 0018-9545
VL - 60
SP - 1799
EP - 1811
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 4
M1 - 5713851
ER -