|Table of Contents|

Citation:
　Yuan Zhang,Chen Guo,Hai Hu,et al.An Algorithm of the Adaptive Grid and Fuzzy Interacting Multiple Model[J].Journal of Marine Science and Application,2014,(3):340-345.[doi:10.1007/s11804-014-1266-6]
Click and Copy

An Algorithm of the Adaptive Grid and Fuzzy Interacting Multiple Model

Info

Title:
An Algorithm of the Adaptive Grid and Fuzzy Interacting Multiple Model
Author(s):
Yuan Zhang;  Chen Guo;  Hai Hu;  Shubo Liu and Junbo Chu
Affilations:
Author(s):
Yuan Zhang;  Chen Guo;  Hai Hu;  Shubo Liu and Junbo Chu
1. College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China 2. Department of Missile, Dalian Naval Academy, Dalian 116018, China
Keywords:
maneuvering target tracking;  adaptive grid;  fuzzy logic inference;  variable structure multiple model;  adaptive grid and fuzzy interacting multiple model (AGFIMM);  interacting multiple model (IMM)
分类号:
-
DOI:
10.1007/s11804-014-1266-6
Abstract:
This paper studies the algorithm of the adaptive grid and fuzzy interacting multiple model (AGFIMM) for maneuvering target tracking, while focusing on the problems of the fixed structure multiple model (FSMM) algorithm’s cost-efficiency ratio being not high and the Markov transition probability of the interacting multiple model (IMM) algorithm being difficult to determine exactly. This algorithm realizes the adaptive model set by adaptive grid adjustment, and obtains each model matching degree in the model set by fuzzy logic inference. The simulation results show that the AGFIMM algorithm can effectively improve the accuracy and cost-efficiency ratio of the multiple model algorithm, and as a result is suitable for engineering applications.

References:

Chen X (2008). The target tracking based on variable structure multiple model algorithm. M.S.degree thesis, Detection Technology and Automation Equipment, Nanjing Science and Technology Univercity, Nanjing, 13-14.(in Chinese)
Gao L, Xing JP, Ma ZL, Sha JC, Meng XZ (2012). Improved IMM algorithm for nonlinear maneuvering target tracking. 2012 International Workshop on Information and Electronics Engineering, Harbin, 4117-4123.
Gong S, Wu HL, Tao C, Huang SG. (2010). Tracking maneuvering target on airport surface based on IMM-UKF algorithm. International Conference on Optoelectronics and Image Processing, Haiko, 671-675.
Guo YF Zhang X, Lin XY (2011). Low altitude maneuvering target tracking with acoustic network based on DS-VSMM. Opto-Electronic Engineering, 38(8), 1-7. (in Chinese)
Huang XY (2010). Highly maneuvering target tracking algorithm based on variable structure multiple-model algorithm. M.S.degree thesis, Pattern Recognition and Intelligent, Electron Science and Technology University of Hangzhou, Wu han, 36-37. (in Chinese)
Huang XY, Peng DL (2010). A VSMM algorithm based on unscented digraph switching for maneuvering target tracking. Opto-Electronic Engineering, 37(12), 30-34.(in Chinese)
Lei SW, Wu CL, Sun W (2010). A method of adaptive maneuvering target tracking based on VSMM. Modern Rada, 32(6), 54-58.
Li XR, Jilkov VP (2005). Survey of maneuvering target tracking. Part V: Multiple-model methods. IEEE Transactions on Aerospace and Electronic Systems, 41(4), 1297-1298.
Li XR, Zhi XR, Zhang YM (1999a). Multiple-model estimation with variable structure. Part III: Model-group switching algorithm. IEEE Transactins on Aerospace and Electronic Systems, 35(1), 225-241.
Li XR, Zhang YM, Zhi XR (1999b). Multiple-model estimation with variable structure. Part IV: Design and evaluation of model-group switching algorithm. IEEE Transactins on Aerospace and Electronic Systems, 35(1), 242-254.
Li XR, Zhang YM (2000). Multiple-model estimation with variable structure. Part V: likely-model set algorithm. IEEE Transactins on Aerospace and Electronic Systems, 36(2), 448-466.
Liu GF, Gu XF, Wang HN (2009). Design and comparison of two MM algorithms for strong maneuvering target tracking. Journal of System Simulation, 21(4), 965-968.
Lu JY (2010). The Research on IMM tracking algorithm of high speed and high maneuvering target. M.S.degree thesis, Guidance, Guidanuce and Control, Nanjing Science and Technology Univercity, Nanjing, 43-44. (in Chinese)
Tanjan H (2011). A switched IMM-extended Viterbi estimator- based algorithm for maneuvering target tracking. Automatica, 47, 92-98.
Vahabian A, Sedigh AK, Akhbardeh A (2004). Optimal design of the variable structure IMM tracking filters using genetic algorithms. Proceeding of the 2004 IEEE International Conference on Control Applications, Taipei, 25-27.
Wang XZ, Subhash C, Rob E (2003). Variable structure IMM using minimal sub-model-set switching. Proceedings of SPIE, 80-91.
Wu PL, Li XX (2009). Passive multi-sensor maneuvering target tracking based on UKF-IMM algorithm. WASE International Conference on Information Engineering, Taiyuan, 135-138.
Wu WR, Cheng PP (1994). A nonlinear IMM algorith for maneuvering target tracking. IEEE Transactions on Aerospace and Electronic Systems, 30, 875-885.
Xu JH, Ji CX, Zhang YS, Chen K (2003). Digraph switching IMM algorithm based current statistical mode. Fire Control & Command Control, 28(2), 52-56.
Yi L, Lv M (2006). Research method for tracking high speed and highly maneuvering target. 6th International Conference on ITS Telecommunications Proceedings, Chengdu, 1236-1239.
Yin HB (2008). The research on radar maneuvering target tracking filter algorithm. M.S.degree thesis, Communication and Information System, Dalian Maritime University, Dalian, 50-51.(in Chinese)
Zeng D, Peng DL (2012). Adaptive variable structure multiple model algorithm for high maneuvering target tracking. Computer System Application, 21(10), 114-117.
Zhang AQ, Wang WS, Zheng RG, Lv J (2011). Research on non-linear filter for naval vessel radar target tracking. Ship Science and Technology, 33(4), 98-101.
Zhang M (2010). Variable structure multiple model estimation based on particle filter. M.S.degree thesis, Communication and Information System, University of Science and Technology of China, Hefei, 10-12. (in Chinese)
Zhen D, Lang H (1998). A distributed IMM fusion algorithm for multi-platform tracking. Signal Processing, 64,167-176.

Memo

Memo:
Supported by the National Nature Science Foundation of China (No. 61074053，61374114) and the Applied Basic Research Program of Ministry of Transport of China (No. 2011-329-225-390).
Last Update: 2014-10-16