TY - GEN
T1 - A Fuzzy Adaptive Strong Tracking Algorithm with Fading Factor
AU - Fang, Shuai
AU - Zhao, Chuchu
AU - Sun, Jinping
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The maximum acceleration parameter determines the effect of the current statistical (CS) model. Thus, when tracking weak maneuvering targets or targets whose actual acceleration exceeds the given value, the tracking performance of the traditional algorithm that sets with a priori fixed value will drop sharply. To solve this problem, a fuzzy adaptive strong tracking algorithm with fading factor (IAFCS-IMM) is proposed. The algorithm adopts a two-level fuzzy logic system. Through the first-level fuzzy logic, a maneuvering factor representing the maneuverability of the target is obtained according to the estimated acceleration information of the model, and the maximum acceleration parameter is adaptively modified. The second-level fuzzy logic is adopted to adjust the model update probability of interacting multiple model (IMM) algorithm according to the maneuver factor. Besides, a fading factor is introduced in the filtering process, which can enhance the robustness of the filter to the sharp mutation of the target state. Simulation results demonstrate that IAFCS-IMM algorithm achieves good results in filtering accuracy and tracking stability of maneuvering targets.
AB - The maximum acceleration parameter determines the effect of the current statistical (CS) model. Thus, when tracking weak maneuvering targets or targets whose actual acceleration exceeds the given value, the tracking performance of the traditional algorithm that sets with a priori fixed value will drop sharply. To solve this problem, a fuzzy adaptive strong tracking algorithm with fading factor (IAFCS-IMM) is proposed. The algorithm adopts a two-level fuzzy logic system. Through the first-level fuzzy logic, a maneuvering factor representing the maneuverability of the target is obtained according to the estimated acceleration information of the model, and the maximum acceleration parameter is adaptively modified. The second-level fuzzy logic is adopted to adjust the model update probability of interacting multiple model (IMM) algorithm according to the maneuver factor. Besides, a fading factor is introduced in the filtering process, which can enhance the robustness of the filter to the sharp mutation of the target state. Simulation results demonstrate that IAFCS-IMM algorithm achieves good results in filtering accuracy and tracking stability of maneuvering targets.
KW - CS model
KW - fuzzy logic
KW - interacting multiple model algorithm
KW - maneuvering target tracking
UR - https://www.scopus.com/pages/publications/85123503566
U2 - 10.1109/CISP-BMEI53629.2021.9624316
DO - 10.1109/CISP-BMEI53629.2021.9624316
M3 - 会议稿件
AN - SCOPUS:85123503566
T3 - Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021
BT - Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021
A2 - Li, Qingli
A2 - Wang, Lipo
A2 - Wang, Yan
A2 - Li, Wenwu
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021
Y2 - 23 October 2021 through 25 October 2021
ER -