TY - JOUR
T1 - Optical Beam Position Tracking in Free-Space Optical Communication Systems
AU - Bashir, Muhammad Salman
AU - Bell, Mark R.
N1 - Publisher Copyright:
© 1965-2011 IEEE.
PY - 2018/4/1
Y1 - 2018/4/1
N2 - Optical beam position on a detector array is an important parameter for optimal symbol detection in a free-space optical communication system; hence it is essential to accurately estimate and track the beam position (which is unknown and may be varying in time). In this paper, we have attempted to solve the beam position tracking problem by setting it up as a state space variable filtering problem in the context of a dynamical system. We propose the following filtering methods for tracking the beam position: a Kalman filter and a particle filter that both use the maximum likelihood estimate of stationary beam position as a measurement in a linear dynamical model setting, and another particle filter which employs the received photon counts as observations for the nonlinear dynamical model. We compare the performance of these three filters in terms of mean-square error assuming that the beam position evolves in time according to a specified model. It is concluded from simulation results that the Kalman filter gives close to optimal performance for high to moderate photon rates for the Gauss-Markov evolution model. However, both the particle filtering algorithms outperform the Kalman filter for the uniformly distributed evolution noise scenario.
AB - Optical beam position on a detector array is an important parameter for optimal symbol detection in a free-space optical communication system; hence it is essential to accurately estimate and track the beam position (which is unknown and may be varying in time). In this paper, we have attempted to solve the beam position tracking problem by setting it up as a state space variable filtering problem in the context of a dynamical system. We propose the following filtering methods for tracking the beam position: a Kalman filter and a particle filter that both use the maximum likelihood estimate of stationary beam position as a measurement in a linear dynamical model setting, and another particle filter which employs the received photon counts as observations for the nonlinear dynamical model. We compare the performance of these three filters in terms of mean-square error assuming that the beam position evolves in time according to a specified model. It is concluded from simulation results that the Kalman filter gives close to optimal performance for high to moderate photon rates for the Gauss-Markov evolution model. However, both the particle filtering algorithms outperform the Kalman filter for the uniformly distributed evolution noise scenario.
KW - Free-space optical communication
KW - optical signal detection
KW - parameter estimation
KW - target tracking
UR - http://www.scopus.com/inward/record.url?scp=85023194094&partnerID=8YFLogxK
U2 - 10.1109/TAES.2017.2723138
DO - 10.1109/TAES.2017.2723138
M3 - Article
AN - SCOPUS:85023194094
VL - 54
SP - 520
EP - 536
JO - IEEE Transactions on Aerospace and Electronic Systems
JF - IEEE Transactions on Aerospace and Electronic Systems
SN - 0018-9251
IS - 2
ER -