TY - JOUR
T1 - Adaptive multiaspect target classification and detection with hidden Markov models
AU - Ji, Shihao
AU - Liao, Xuejun
AU - Carin, Lawrence
N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09
PY - 2005/10/1
Y1 - 2005/10/1
N2 - Target detection and classification are considered based on backscattered signals observed from a sequence of target-sensor orientations, with the measurements performed as a function of orientation (angle) at a fixed range. The theory of optimal experiments is applied to adaptively optimize the sequence of target-sensor orientations considered. This is motivated by the fact that if fewer, better-chosen measurements are used then targets can be recognized more accurately with less time and expense. Specifically, based on the previous sequence of observations Ot = {O1,..., Ot}, the technique determines what change in relative target-sensor orientation Δθt+1 is optimal for performing measurement t + 1, to yield observation Ot+1. The target is assumed distant or hidden, and, therefore, the absolute target-sensor orientation is unknown. We detail the adaptive-sensing algorithm, employing a hidden Markov model representation of the multiaspect scattered fields, and example classification and detection results are presented for underwater targets using acoustic scattering data. © 2005 IEEE.
AB - Target detection and classification are considered based on backscattered signals observed from a sequence of target-sensor orientations, with the measurements performed as a function of orientation (angle) at a fixed range. The theory of optimal experiments is applied to adaptively optimize the sequence of target-sensor orientations considered. This is motivated by the fact that if fewer, better-chosen measurements are used then targets can be recognized more accurately with less time and expense. Specifically, based on the previous sequence of observations Ot = {O1,..., Ot}, the technique determines what change in relative target-sensor orientation Δθt+1 is optimal for performing measurement t + 1, to yield observation Ot+1. The target is assumed distant or hidden, and, therefore, the absolute target-sensor orientation is unknown. We detail the adaptive-sensing algorithm, employing a hidden Markov model representation of the multiaspect scattered fields, and example classification and detection results are presented for underwater targets using acoustic scattering data. © 2005 IEEE.
UR - http://ieeexplore.ieee.org/document/1504766/
UR - http://www.scopus.com/inward/record.url?scp=27744432266&partnerID=8YFLogxK
U2 - 10.1109/JSEN.2005.847936
DO - 10.1109/JSEN.2005.847936
M3 - Article
SN - 1530-437X
VL - 5
SP - 1035
EP - 1042
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 5
ER -