TY - GEN
T1 - Marked poisson point process PHD filter for DOA tracking
AU - Saucan, Augustin Alexandru
AU - Chonavel, Thierry
AU - Sintes, Christophe
AU - Le Caillec, Jean Marc
N1 - Publisher Copyright:
© 2015 EURASIP.
PY - 2015/12/22
Y1 - 2015/12/22
N2 - In this paper we propose a Track Before Detect (TBD) filter for Direction Of Arrival (DOA) tracking of multiple targets from phased-array observations. The phased-array model poses a new problem since each target emits a signal, called source signal. Existing methods consider the source signal as part of the system state. This is inefficient, especially for particle approximations of posteriors, where samples are drawn from the higher-dimensional posterior of the extended state. To address this problem, we propose a novel Marked Poisson Point Process (MPPP) model and derive the Probability Hypothesis Density (PHD) filter that adaptively estimates target DOAs. The PPP models variations of both the number and the location of points representing targets. The mark of a point represents the source signal, without the need of an extended state. Recursive formulas for the MPPP PHD filter are derived with simulations showcasing improved performance over state-of-the art methods.
AB - In this paper we propose a Track Before Detect (TBD) filter for Direction Of Arrival (DOA) tracking of multiple targets from phased-array observations. The phased-array model poses a new problem since each target emits a signal, called source signal. Existing methods consider the source signal as part of the system state. This is inefficient, especially for particle approximations of posteriors, where samples are drawn from the higher-dimensional posterior of the extended state. To address this problem, we propose a novel Marked Poisson Point Process (MPPP) model and derive the Probability Hypothesis Density (PHD) filter that adaptively estimates target DOAs. The PPP models variations of both the number and the location of points representing targets. The mark of a point represents the source signal, without the need of an extended state. Recursive formulas for the MPPP PHD filter are derived with simulations showcasing improved performance over state-of-the art methods.
KW - DBSCAN
KW - DOA tracking
KW - PHD filter
KW - marked Poisson point process
KW - track before detect
U2 - 10.1109/EUSIPCO.2015.7362859
DO - 10.1109/EUSIPCO.2015.7362859
M3 - Conference contribution
AN - SCOPUS:84963940302
T3 - 2015 23rd European Signal Processing Conference, EUSIPCO 2015
SP - 2621
EP - 2625
BT - 2015 23rd European Signal Processing Conference, EUSIPCO 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd European Signal Processing Conference, EUSIPCO 2015
Y2 - 31 August 2015 through 4 September 2015
ER -