TY - GEN
T1 - Unscented Kalman filter for arbitrary step randomly delayed measurements
AU - Yadav, Ajay Kumar
AU - Mishra, Vikas Kumar
AU - Singh, Abhinoy Kumar
AU - Bhaumik, Shovan
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/2/7
Y1 - 2017/2/7
N2 - The conventional Bayesian framework of filtering is based on the assumption that the measurements are available at each time-step without any delay. But in real-life problems, measurements may be randomly delayed in time. In this paper, we modified the unscented Kalman filter (UKF) for arbitrary time delayed measurements. With the help of simulation results, it has been shown that the proposed filter provides more accurate estimation compared to the ordinary UKF in presence of randomly delayed measurements.
AB - The conventional Bayesian framework of filtering is based on the assumption that the measurements are available at each time-step without any delay. But in real-life problems, measurements may be randomly delayed in time. In this paper, we modified the unscented Kalman filter (UKF) for arbitrary time delayed measurements. With the help of simulation results, it has been shown that the proposed filter provides more accurate estimation compared to the ordinary UKF in presence of randomly delayed measurements.
UR - http://www.scopus.com/inward/record.url?scp=85015728088&partnerID=8YFLogxK
U2 - 10.1109/INDIANCC.2017.7846456
DO - 10.1109/INDIANCC.2017.7846456
M3 - Conference contribution
AN - SCOPUS:85015728088
T3 - 2017 Indian Control Conference, ICC 2017 - Proceedings
SP - 82
EP - 86
BT - 2017 Indian Control Conference, ICC 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers
T2 - 3rd Indian Control Conference, ICC 2017
Y2 - 4 January 2017 through 6 January 2017
ER -