TY - GEN
T1 - Fitting for smoothing
T2 - 2016 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2016
AU - Li, Tiancheng
AU - Prieto, Javier
AU - Corchado, Juan Manuel
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/14
Y1 - 2016/11/14
N2 - A preliminary framework for inferring continuoustime target trajectory (namely "track") is given for a class of target tracking problems in which the target is subject to a rather smooth evolving process in time series, such as tracking passenger aircrafts or ships that have scheduled routes. As the core idea, the distant estimates given over time by a recursive estimator are 'fitted' by using a function of continuous-time, which can be then used to infer the state for any time instants in the effective fitting period, either the past (like conventional smoothing, but curried out online) or the future (including long-term prediction). This regression analysis methodology, referred to as fitting for smoothing (F4S), also facilitates combating misdetection and outliers from which most existing tracking systems suffer. Simulations are provided to illustrate how it works and benefits in either cluttered or non-cluttered environments, with either a single target or multiple targets.
AB - A preliminary framework for inferring continuoustime target trajectory (namely "track") is given for a class of target tracking problems in which the target is subject to a rather smooth evolving process in time series, such as tracking passenger aircrafts or ships that have scheduled routes. As the core idea, the distant estimates given over time by a recursive estimator are 'fitted' by using a function of continuous-time, which can be then used to infer the state for any time instants in the effective fitting period, either the past (like conventional smoothing, but curried out online) or the future (including long-term prediction). This regression analysis methodology, referred to as fitting for smoothing (F4S), also facilitates combating misdetection and outliers from which most existing tracking systems suffer. Simulations are provided to illustrate how it works and benefits in either cluttered or non-cluttered environments, with either a single target or multiple targets.
KW - Long-term prediction
KW - Regression
KW - Target tracking
KW - Time series fitting
KW - Track estimation
UR - http://www.scopus.com/inward/record.url?scp=85003967152&partnerID=8YFLogxK
U2 - 10.1109/IPIN.2016.7743582
DO - 10.1109/IPIN.2016.7743582
M3 - 会议稿件
AN - SCOPUS:85003967152
T3 - 2016 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2016
BT - 2016 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2016
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 October 2016 through 7 October 2016
ER -