TY - JOUR
T1 - Multi-EAP
T2 - Extended EAP for multi-estimate extraction for SMC-PHD filter
AU - Li, Tiancheng
AU - Corchado, Juan M.
AU - Sun, Shudong
AU - Fan, Hongqi
N1 - Publisher Copyright:
© 2016 Chinese Society of Aeronautics and Astronautics
PY - 2017/2/1
Y1 - 2017/2/1
N2 - The ability to extract state-estimates for each target of a multi-target posterior, referred to as multi-estimate extraction (MEE), is an essential requirement for a multi-target filter, whose key performance assessments are based on accuracy, computational efficiency and reliability. The probability hypothesis density (PHD) filter, implemented by the sequential Monte Carlo approach, affords a computationally efficient solution to general multi-target filtering for a time-varying number of targets, but leaves no clue for optimal MEE. In this paper, new data association techniques are proposed to distinguish real measurements of targets from clutter, as well as to associate particles with measurements. The MEE problem is then formulated as a family of parallel single-estimate extraction problems, facilitating the use of the classic expected a posteriori (EAP) estimator, namely the multi-EAP (MEAP) estimator. The resulting MEAP estimator is free of iterative clustering computation, computes quickly and yields accurate and reliable estimates. Typical simulation scenarios are employed to demonstrate the superiority of the MEAP estimator over existing methods in terms of faster processing speed and better estimation accuracy.
AB - The ability to extract state-estimates for each target of a multi-target posterior, referred to as multi-estimate extraction (MEE), is an essential requirement for a multi-target filter, whose key performance assessments are based on accuracy, computational efficiency and reliability. The probability hypothesis density (PHD) filter, implemented by the sequential Monte Carlo approach, affords a computationally efficient solution to general multi-target filtering for a time-varying number of targets, but leaves no clue for optimal MEE. In this paper, new data association techniques are proposed to distinguish real measurements of targets from clutter, as well as to associate particles with measurements. The MEE problem is then formulated as a family of parallel single-estimate extraction problems, facilitating the use of the classic expected a posteriori (EAP) estimator, namely the multi-EAP (MEAP) estimator. The resulting MEAP estimator is free of iterative clustering computation, computes quickly and yields accurate and reliable estimates. Typical simulation scenarios are employed to demonstrate the superiority of the MEAP estimator over existing methods in terms of faster processing speed and better estimation accuracy.
KW - Data association
KW - EAP estimator
KW - Multi-target tracking
KW - Particle filter
KW - PHD filter
UR - http://www.scopus.com/inward/record.url?scp=85010044830&partnerID=8YFLogxK
U2 - 10.1016/j.cja.2016.12.025
DO - 10.1016/j.cja.2016.12.025
M3 - 文章
AN - SCOPUS:85010044830
SN - 1000-9361
VL - 30
SP - 368
EP - 379
JO - Chinese Journal of Aeronautics
JF - Chinese Journal of Aeronautics
IS - 1
ER -