UKF参数辨识的T-S模糊多模型目标跟踪算法
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摘要
针对非线性系统中机动目标动态模型不确定性问题,提出了一种新的基于UKF参数辨识的T-S模糊多模型机动目标跟踪算法。在提出的算法中,用多个语义模糊集对目标特征信息进行模糊表示,构建一个通用的T-S模糊语义多模型框架。在T-S模糊语义多模型中,使用模糊C回归聚类算法实现对前件参数的辨识,同时,为了实现系统的非线性特征,引入无迹卡尔曼算法辨识后件参数。仿真结果表明,提出的算法跟踪性能优于传统的交互多模型算法和交互多模型无迹卡尔曼滤波算法,在被跟踪目标突然发生方向改变或目标的动态先验信息不精确等复杂情况时,能够有效地对目标进行精确跟踪。
A novel T-S Fuzzy Multiple Model Target Tracking Algorithm with UKF Parameter Identification(TS-UKF)is proposed to solve the uncertainty problem of maneuvering target dynamic model in nonlinear systems. Firstly, the target feature information is represented by multiple semantic fuzzy sets, and a general T-S fuzzy semantic multiple model framework is constructed. Then, the fuzzy C regression clustering algorithm is used to identify the premise parameters of the T-S fuzzy semantic multiple model. Meanwhile, to realize the nonlinear characteristics of the system, the unscented Kalman filtering algorithm is introduced to identify the consequence parameters. Simulation results show that the proposed algorithm has better tracking performance than the traditional interacting multiple model algorithm and interacting multiple model unscented Kalman filter. When the direction of the target is changed suddenly or the dynamic prior information of the target is not accurate, it can effectively track the target.
A novel T-S Fuzzy Multiple Model Target Tracking Algorithm with UKF Parameter Identification(TS-UKF)is proposed to solve the uncertainty problem of maneuvering target dynamic model in nonlinear systems. Firstly, the target feature information is represented by multiple semantic fuzzy sets, and a general T-S fuzzy semantic multiple model framework is constructed. Then, the fuzzy C regression clustering algorithm is used to identify the premise parameters of the T-S fuzzy semantic multiple model. Meanwhile, to realize the nonlinear characteristics of the system, the unscented Kalman filtering algorithm is introduced to identify the consequence parameters. Simulation results show that the proposed algorithm has better tracking performance than the traditional interacting multiple model algorithm and interacting multiple model unscented Kalman filter. When the direction of the target is changed suddenly or the dynamic prior information of the target is not accurate, it can effectively track the target.
引文
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[19] Li Yanhui,Li Yulong.Robust L1 output tracking control for uncertain networked control systems described by T-S fuzzy model with distributed delays[J].International Journal of Systems Science,2017,48(15):1-9.
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[2] Khalid S S,Abrar S.A low-complexity interacting multiple model filter for maneuvering target tracking[J].AEUE-International Journal of Electronics and Communications,2017,73(3):157-164.
[3] Chen Xulong,Gao Jian,Han Xing.An algorithm based on interacting multiple models for maneuvering target tracking[C]//Information Technology and Artificial Intelligence Conference.IEEE,2015:405- 408.
[4] Chang Lubin,Hu Baiqing,Li Kailong.Iterated multiplicative extended kalman filter for attitude estimation using vector observations[J].Sensors,2018,18(5):2053-2060.
[5] Kim B,Yi K,Yoo H,Chong H.An IMM/EKF Approach for Enhanced Multitarget State Estimation for Application to Integrated Risk Management System[J].Vehicular Technology IEEE Transactions on,2015,64(3):876- 889.
[6] Gao Bingbing,Hu Gaoge,Gao Shesheng,et al.Multi-sensor Optimal Data Fusion for INS/GNSS/CNS Integration Based on Unscented Kalman Filter[J].International Journal of Control Automation & Systems,2018,16(1):129-140.
[7] Qi Junjian,Sun Kai,Wang Jianhui,et al.Dynamic State Estimation for Multi-Machine Power System by Unscented Kalman Filter With Enhanced Numerical Stability[J].IEEE Transactions on Smart Grid,2018,9(2):1184-1196.
[8] Julier S J,Uhlmann J K.Unscented filtering and nonlinear estimation[J].Proceedings of the IEEE,2004,92(3):401- 422.
[9] ángel F.Garcia-Fernandez,Morelande M R,Grajal J.Truncated Unscented Kalman Filtering[J].IEEE Transactions on Signal Processing,2012,60(7):3372-3386.
[10] Zhan Ronghui,Wan Jianwei.Iterated Unscented Kalman Filter for Passive Target Tracking[J].Aerospace & Electronic Systems IEEE Transactions on,2007,43(3):1155-1163.
[11] Kottakki K K,Bhushan M,Bhartiya S.An Improved Gaussian Sum Unscented Kalman Filter[J].IFAC Proceedings Volumes,2014,47(1):355-362.
[12] Xu Qimin,Li Xu,Chan Chingyao.A Cost-Effective Vehicle Localization Solution Using an Interacting Multiple Model-Unscented Kalman Filters(IMM-UKF)Algorithm and Grey Neural Network[J].Sensors,2017,17(6):1431.
[13] Gao Bingbing,Gao Shesheng,Zhong Yongmin,et al.Interacting multiple model estimation-based adaptive robust unscented Kalman filter[J].International Journal of Control Automation & Systems,2017,15(2):1-13.
[14] Pan Yi,Ye Hui,He Keke.Spherical Simplex Unscented Kalman Filter-Based Jumping and Static Interacting Multiple Model[J].International Journal of Pattern Recognition and Artificial Intelligence,2016,32(4):89-92.
[15] Jwo Dah-Jing,Tseng Chien-Hao.Fuzzy adaptive interacting multiple model unscented Kalman filter for integrated navigation[C]//Control Applications.IEEE,2009:1684-1689.
[16] Hua Haochen,Cao Junwei,Yang Gang,et al.Voltage Control for Uncertain Stochastic Nonlinear System with Application to Energy Internet:Non-fragile Robust H ∞ Approach[J].Journal of Mathematical Analysis & Applications,2018,463(1):93-110.
[17] Mizumoto I,Kawabe N.Adaptive output feedback based output tracking control for uncertain nonlinear systems via T-S fuzzy model[C]//IEEE,Conference on Decision and Control.IEEE,2017:891- 896.
[18] Chang Chia-Wen,Tao Chin-Wang:A Novel Approach to Implement Takagi-Sugeno Fuzzy Models[J].IEEE Transactions on Cybernetics,2017,47(9):2353-2361.
[19] Li Yanhui,Li Yulong.Robust L1 output tracking control for uncertain networked control systems described by T-S fuzzy model with distributed delays[J].International Journal of Systems Science,2017,48(15):1-9.
[20] Xie Xiaolong,Lin Lin,Zhong Shisheng.Process Takagi-Sugeno model:A novel approach for handling continuous input and output functions and its application to time series prediction[J].Knowledge-Based Systems,2014,63(3):46-58.