A Sugeno-type neuro-fuzzy adaptive filter for online maneuvering target tracking

Author

Menhaj, Mohammad B. ; Amani, Soheil

Author_Institution

Dept. of Electr. Eng., Amirkabir Univ., Tehran, Iran

Volume

4

fYear

2001

fDate

2001

Firstpage

2941

Abstract

A neuro-fuzzy adaptive filter employing Sugeno-type If-Then rules with online structure and parameter learning capability is developed for the online maneuvering target tracking problem. The maneuver is considered as an inherent part of the target dynamics; this makes the system nonstationary. To show the performance of the proposed filter, we use the same dynamic model of the MA-2d radar for comparison with the interacting multiple model techniques. The performance of the designed filter was evaluated by Monte Carlo simulation over a test trajectory. The results show the effectiveness of the proposed filter

Keywords

adaptive filters; filtering theory; fuzzy neural nets; learning (artificial intelligence); radar tracking; real-time systems; target tracking; MA-2d radar; Sugeno-type fuzzy rules; adaptive filter; dynamic model; fuzzy neural network; interacting multiple model; maneuvering target tracking; parameter learning; structure learning; Adaptive filters; Clustering algorithms; Degradation; Filtering; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Humans; Military standards; Target tracking;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-7044-9

Type

conf

DOI

10.1109/IJCNN.2001.938845

Filename

938845