TY - GEN
T1 - A new active and passive asynchronous fusion tracking of maneuvering target
AU - Zhang, Wei
AU - Jiang, Hong
AU - Song, Long
AU - Ren, Zhang
PY - 2006
Y1 - 2006
N2 - A new asynchronous data fusion algorithm was adopted in this paper. This new fusion algorithm was based on the feedback fusion and the delicate time-slicing. Its basic idea is: at instants with neither passive (PTT) nor active target track-ing (ATT) measurements, it will predict this instant's states based on last instant's filtering result; at instants with only active or passive target tracking measurement, it will use its own filtering algorithm i.e., Converted Measurement Kalma-n Filter (CMKF) for active radar, Pseudo Linear Estimator (PLE) with frequency information for passive radar to get this instant's states; at instants with both passive and active target tracking measurements, it will fuse the active and passive measurements by feedback fusion algorithm, with the passive radar receiving the feedback information. The advantage of this new algorithm is that it can fuse the active and passive target tracking with non multiple integer sampling period ratio. This paper also extended the 2D constant velocity moving target to the 3D maneuvering target. Simulation results indicate that the new fusion algorithm can improve the tracking performance in some acceptable degree.
AB - A new asynchronous data fusion algorithm was adopted in this paper. This new fusion algorithm was based on the feedback fusion and the delicate time-slicing. Its basic idea is: at instants with neither passive (PTT) nor active target track-ing (ATT) measurements, it will predict this instant's states based on last instant's filtering result; at instants with only active or passive target tracking measurement, it will use its own filtering algorithm i.e., Converted Measurement Kalma-n Filter (CMKF) for active radar, Pseudo Linear Estimator (PLE) with frequency information for passive radar to get this instant's states; at instants with both passive and active target tracking measurements, it will fuse the active and passive measurements by feedback fusion algorithm, with the passive radar receiving the feedback information. The advantage of this new algorithm is that it can fuse the active and passive target tracking with non multiple integer sampling period ratio. This paper also extended the 2D constant velocity moving target to the 3D maneuvering target. Simulation results indicate that the new fusion algorithm can improve the tracking performance in some acceptable degree.
KW - Active target tracking (ATT)
KW - Converted Measurement Kalman Filter (CMKF)
KW - Data fusion
KW - Delicate time-slicing
KW - Feedback fusion
KW - Passive target tracking (PTT)
KW - Pseudo Linear Estimator (PLE) with frequency information
UR - https://www.scopus.com/pages/publications/33846567565
U2 - 10.1117/12.718385
DO - 10.1117/12.718385
M3 - 会议稿件
AN - SCOPUS:33846567565
SN - 0819464538
SN - 9780819464538
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Sixth International Symposium on Instrumentation and Control Technology
T2 - Sixth International Symposium on Instrumentation and Control Technology: Sensors, Automatic Measurement, Control, and Computer Simulation
Y2 - 13 October 2006 through 15 October 2006
ER -