TY - GEN
T1 - Random-Matrix Based Extended Object Tracking Using Multiple Sensors with Different Measurement Matrices
AU - Zhang, Xiaoxiao
AU - Lan, Jian
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Random-matrix based extended object tracking (EOT) has received much attention for the simple yet effective scheme. Multiple sensors can bring more information due to the different perspectives and characteristics especially for EOT. However, in practice, multiple sensors have different measurement matrices, which makes EOT even more challenging. This paper develops a variational Bayesian approach to multi-sensor EOT (VB-MS-EOT) based on random matrix. First, the system model of MS-EOT with different measurement matrices is developed. This model also considers the differently distorted observations and various measurement numbers of multiple sensors. To deal with the different measurement matrices and distortions, VB-MS-EOT is proposed, where the kinematic state and extension are estimated iteratively with a simple form. Compared with the traditional EOT based on random matrix using multiple sensors with identical measurement matrix, the effectiveness of VB-MS-EOT is illustrated by simulated data.
AB - Random-matrix based extended object tracking (EOT) has received much attention for the simple yet effective scheme. Multiple sensors can bring more information due to the different perspectives and characteristics especially for EOT. However, in practice, multiple sensors have different measurement matrices, which makes EOT even more challenging. This paper develops a variational Bayesian approach to multi-sensor EOT (VB-MS-EOT) based on random matrix. First, the system model of MS-EOT with different measurement matrices is developed. This model also considers the differently distorted observations and various measurement numbers of multiple sensors. To deal with the different measurement matrices and distortions, VB-MS-EOT is proposed, where the kinematic state and extension are estimated iteratively with a simple form. Compared with the traditional EOT based on random matrix using multiple sensors with identical measurement matrix, the effectiveness of VB-MS-EOT is illustrated by simulated data.
KW - different measurement matrices
KW - extended object tracking
KW - multisensor measurements
KW - random matrix
UR - https://www.scopus.com/pages/publications/85191437594
U2 - 10.1109/ICARCE59252.2024.10492478
DO - 10.1109/ICARCE59252.2024.10492478
M3 - 会议稿件
AN - SCOPUS:85191437594
T3 - ICARCE 2023 - 2023 2nd International Conference on Automation, Robotics and Computer Engineering
BT - ICARCE 2023 - 2023 2nd International Conference on Automation, Robotics and Computer Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Automation, Robotics and Computer Engineering, ICARCE 2023
Y2 - 14 December 2023 through 16 December 2023
ER -