| [1] |
KIRUBARAJAN T , SHALOM Y B . Kalman filter versus IMM estimator:when do we need the latter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003,39(4): 1452-1457.
|
| [2] |
KAPPL J J . Nonlinear estimation via Kalman filtering[J]. IEEE Transactions on Aerospace and Electronic Systems, 1971,7(1): 79-84.
|
| [3] |
魏星, 万建伟, 皇甫堪 . 基于粒子滤波的单站无源定位跟踪新算法研究[J]. 通信学报, 2005,26(12): 81-85.
|
|
WEI X , WAN J W , HUANG F K . New technique in single observer passive tracking based on particle filter[J]. Journal on Communications, 2005,26(12): 81-85.
|
| [4] |
XIE A , YIN F , AI B ,et al. Learning while tracking:a practical system based on variational Gaussian process state-space model and smartphone sensory data[C]// Proceedings of 2020 IEEE 23rd International Conference on Information Fusion. Piscataway:IEEE Press, 2020: 1-7.
|
| [5] |
MA Y J , ZHAO S Y , HUANG B . Multiple-model state estimation based on variational Bayesian inference[J]. IEEE Transactions on Automatic Control, 2019,64(4): 1679-1685.
|
| [6] |
LIM J , KIM H S , PARK H M . Interactive-multiple-model algorithm based on minimax particle filtering[J]. IEEE Signal Processing Letters, 2020,27: 36-40.
|
| [7] |
AFTAB W , MIHAYLOVA L . A learning Gaussian process approach for maneuvering target tracking and smoothing[J]. IEEE Transactions on Aerospace and Electronic Systems, 2021,57(1): 278-292.
|
| [8] |
DOUCET A.RISTIC B . Recursive state estimation for multiple switching models with unknown transition probabilities[J]. IEEE Transactions on Aerospace and Electronic Systems, 2002,38(3): 1098-1104.
|
| [9] |
SADEGHZADEH-NOKHODBERIZ N , POSHTAN J . Distributed interacting multiple filters for fault diagnosis of navigation sensors in a robotic system[J]. IEEE Transactions on Systems,Man,and Cybernetics:Systems, 2017,47(7): 1383-1393.
|
| [10] |
郭志, 董春云, 蔡远利 ,等. 时变转移概率 IMM-SRCKF 机动目标跟踪算法[J]. 系统工程与电子技术, 2015,37(1): 24-30.
|
|
GUO Z , DONG C Y , CAI Y L ,et al. Time-varying transition probability based IMM-SRCKF algorithm for maneuvering target tracking[J]. Systems Engineering and Electronics, 2015,37(1): 24-30.
|
| [11] |
QI S Y , QI C D , WANG W H . Maneuvering target tracking algorithm based on adaptive Markov transition probabilitiy matrix and IMM-MGEKF[C]// Proceedings of 2018 12th International Symposium on Antennas,Propagation and EM Theory (ISAPE). Piscataway:IEEE Press, 2018: 1-4.
|
| [12] |
LUAN Z Z , GU B , WANG J F . A real-time interactive multi-model (RT-IMM) target tracking method[C]// Proceedings of 2021 IEEE International Conference on Artificial Intelligence and Computer Applications. Piscataway:IEEE Press, 2021: 503-507.
|
| [13] |
许登荣, 程水英, 包守亮 . 自适应转移概率交互式多模型跟踪算法[J]. 电子学报, 2017,45(9): 2113-2120.
|
|
XU D R , CHENG S Y , BAO S L . Interacting multiple model algorithm based on adaptive transition probability[J]. Acta Electronica Sinica, 2017,45(9): 2113-2120.
|
| [14] |
YOUN W , KO N Y , GADSDEN S A ,et al. A novel multiple-model adaptive Kalman filter for an unknown measurement loss probability[J]. IEEE Transactions on Instrumentation and Measurement, 2021,70: 1-11.
|
| [15] |
HAN B , HUANG H Q , LEI L ,et al. An improved IMM algorithm based on STSRCKF for maneuvering target tracking[J]. IEEE Access, 2019,7: 57795-57804.
|
| [16] |
MA J , GUO X T . Combination of IMM algorithm and ASTRWCKF for maneuvering target tracking[J]. IEEE Access, 2020,8: 143095-143103.
|
| [17] |
戴定成, 姚敏立, 蔡宗平 ,等. 改进的马尔可夫参数自适应IMM算法[J]. 电子学报, 2017,45(5): 1198-1205.
|
|
DAI D C , YAO M L , CAI Z P ,et al. Improved adaptive Markov IMM algorithm[J]. Acta Electronica Sinica, 2017,45(5): 1198-1205.
|
| [18] |
XIE G , SUN L L , WEN T ,et al. Adaptive transition probability matrix-based parallel IMM algorithm[J]. IEEE Transactions on Systems,Man,and Cybernetics:Systems, 2021,51(5): 2980-2989.
|
| [19] |
RONG LI X , JILKOV V P . Survey of maneuvering target tracking (part I.dynamic models)[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003,39(4): 1333-1364.
|
