An innovative fuzzy covariance presetting for high maneuvering target tracking problems

Beheshtipour, Zohreh; Khaloozadeh, Hamid

doi:10.1109/ccdc.2009.5195200

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…More details about AUKF can be found in Karsaz, 2009, Bahari et al, 2009a,b;Beheshtipour and Khaloozadeh, 2009;Yang and ji, 2010).…”

Section: The Aukf Algorithmmentioning

confidence: 99%

“…To aid the IE approaches to cope with this trouble, Khaloozadeh and Karsaz (2009) have recently proposed a new SKF-based target tracker with IE approach. This method is an Augmented Kalman Filter (AUKF), and it has obtained lots of attention Bahari et al, , 2011Beheshtipour and Khaloozadeh, 2009;Yang and Ji, 2010) due to the elimination of the constant input assumption in the previous IE approaches. The AUKF has a special form in the state space equation and estimates target acceleration along with the other states, simultaneously.…”

Section: The Ie Approachmentioning

confidence: 99%

See 1 more Smart Citation

An IMM algorithm based on augmented kalman filter for maneuvering target tracking

Amirzadeh¹,

Karimpour²,

Moeini³

2011

Sci. Res. Essays

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In this paper, in order to increase the accuracy of interacting multiple model (IMM) algorithm in presence of low and medium maneuvers, a new IMM algorithm based on Augmented Kalman Filter (AUKF) has been proposed. The accuracy of the IMM algorithm depends upon having a set of filters with motion models which are similar at all times to the real target situations. One way to increase the accuracy of this estimator is to substitute more accurate filters instead of the Standard Kalman Filter (SKF) in it. In order to improve the performance of IMM algorithm, this paper proposes to replace each SKF in the IMM algorithm with the AUKF. Due to the better accuracy of the AUKF than SKF in presence of low and medium maneuvers, this substitution will improve the performance of IMM algorithm in these maneuvering levels. The Monte-Carlo simulation results show the accuracy of the proposed method.

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“…More details about AUKF can be found in Karsaz, 2009, Bahari et al, 2009a,b;Beheshtipour and Khaloozadeh, 2009;Yang and ji, 2010).…”

Section: The Aukf Algorithmmentioning

confidence: 99%

Section: The Ie Approachmentioning

confidence: 99%