TY - GEN
T1 - MAP estimator for target tracking in wireless sensor networks for unknown transmit power
AU - Tomic, Slavisa
AU - Beko, Marko
AU - Dinis, Rui
AU - Tuba, Milan
AU - Bacanin, Nebojsa
N1 - This work was partially supported by Fundação para a Ciência e a Tecnologia under Project PEst-OE/EEI/UI0066/2014 (UNINOVA), and Project UID/EEA/50008/2013 (Instituto de Telecomunicações), Program Investigador FCT under Grant IF/00325/2015 and Grant SFRH/BD/91126/2012.
PY - 2017
Y1 - 2017
N2 - This paper addresses the target tracking problem, by extracting received signal strength (RSS) and angle of arrival (AoA) information from the received radio signal, in the case where the target transmit power is considered unknown. By combining the radio observations with prior knowledge given by the target transition state model, we apply the maximum a posteriori (MAP) criterion to the marginal posterior distribution function (PDF). However, the derived MAP estimator cannot be solved directly, so we tightly approximate it for small noise power. The target state estimate is then easily obtained at any time step by employing a recursive approach, typical for Bayesian methods. Our simulations confirm the effectiveness of the proposed algorithm, offering good estimation accuracy in all considered scenarios.
AB - This paper addresses the target tracking problem, by extracting received signal strength (RSS) and angle of arrival (AoA) information from the received radio signal, in the case where the target transmit power is considered unknown. By combining the radio observations with prior knowledge given by the target transition state model, we apply the maximum a posteriori (MAP) criterion to the marginal posterior distribution function (PDF). However, the derived MAP estimator cannot be solved directly, so we tightly approximate it for small noise power. The target state estimate is then easily obtained at any time step by employing a recursive approach, typical for Bayesian methods. Our simulations confirm the effectiveness of the proposed algorithm, offering good estimation accuracy in all considered scenarios.
KW - Angle of arrival (AoA)
KW - Maximum a posteriori (MAP) estimator
KW - Received signal strength (RSS)
KW - Target tracking
UR - http://www.scopus.com/inward/record.url?scp=85018183086&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-56077-9_32
DO - 10.1007/978-3-319-56077-9_32
M3 - Conference contribution
AN - SCOPUS:85018183086
SN - 978-3-319-56076-2
T3 - IFIP Advances in Information and Communication Technology
SP - 325
EP - 334
BT - Technological Innovation for Smart Systems - 8th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2017, Proceedings
A2 - Camarinha-Matos , L. M.
A2 - Parreira-Rocha, M.
A2 - Ramezani , J.
PB - Springer
CY - Cham
T2 - 8th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2017
Y2 - 3 May 2017 through 5 May 2017
ER -