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Estimating directional data from network topology for improving tracking performance
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Orientador(es)
Resumo(s)
This work proposes a novel approach for tracking a moving target in non-line-of-sight (NLOS) environments based on range estimates extracted from received signal strength (RSS) and time of arrival (TOA) measurements. By exploiting the known architecture of reference points to act as an improper antenna array and the range estimates, angle of arrival (AOA) of the signal emitted by the target is first estimated at each reference point. We then show how to take advantage of these angle estimates to convert the problem into a more convenient, polar space, where a linearization of the measurement models is easily achieved. The derived linear model serves as the main building block on top of which prior knowledge acquired during the movement of the target is incorporated by adapting a Kalman filter (KF). The performance of the proposed approach was assessed through computer simulations, which confirmed its effectiveness in combating the negative effect of NLOS bias and superiority in comparison with its naive counterpart, which does not take prior knowledge into consideration.
Descrição
UID/MULTI/04111/0213
UID/MULTI/04111/0216
PCIF/SSI/0102/2017
UID/EEA/50008/2019
PES3N POCI-01-0145-FEDER-030629
Grant IF/00325/2015
Palavras-chave
Angle of arrival (AOA) Kalman filter (KF) Non-line-of-sight (NLOS) Received signal strength (RSS) Target tracking Time of arrival (TOA) Instrumentation Computer Networks and Communications Control and Optimization