Content Provider	IEEE Xplore Digital Library
Author	Aly, S.M. El Fouly, R. Braka, H.
Copyright Year	2009
Description	Author affiliation: Computer Engineering Dept. Cairo, University Faculty of engineering, Giza Egypt (El Fouly, R.; Braka, H.) \|\| Computer Engineering Dept., Modern Academy in Maadi for Engineering and Technology Egypt (Aly, S.M.)
Abstract	This paper consider the nonlinear state estimate problem for tracking maneuvering targets. Two methods are introduced to overcome the difficulty of non-linear model. The first method uses Interacting Multiple Model (IMM) which includes 2, 3, 4 and 10 models. These models are linear, each model stands for an operation point of the nonlinear model. Two model sets are designed using Equal-Distance Model-Set Design for each. The effect of increasing the number of models, separation between them and noise effect on the accuracy is introduced. The second method uses Second order Extended Kalman Filter (EKF2) which is a single nonlinear filter. Both methods are evaluated by simulation using two scenarios. A comparison between them is evaluated by computing their accuracy, change of operation range and computational complexity (computational time) at different measurement noise. Based on this study for small range of variation of nonlinear parameter, and low noise the EKF2 introduced quick and accurate tracking. For a large range of nonlinearity and good separation between models of IMM, at minimum noise large and small numbers of models of IMM introduced best accuracy but as the noise increase large number keeps higher accuracy until the large numbers and small numbers of IMM introduced bad accuracy. At high noise optimizing number of models and separation between model sets, IMM introduces better accuracy.
Starting Page	149
Ending Page	156
File Size	647544
Page Count	8
File Format	PDF
ISBN	9781424448388
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Universita Politecnica Delle
Subject Keyword	Target tracking Filtering Military computing Sensor Networks Sensor fusion Ad hoc networks Extended Kalman filter Interacting Multiple Model (IMM) Wireless sensor networks Vehicle detection Probabilistic Data Association Collaboration Computer networks Kalman filters
Content Type	Text
Resource Type	Article

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