Tracking a maneuvering target using input estimation versus the interacting multiple model algorithm

Bar‐Shalom, Yaakov; Chang, Kuo‐Chu; Blom, H.A.P.

doi:10.1109/7.18693

Cited by 286 publications

(106 citation statements)

References 16 publications

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“…Figure 1 shows the structure of the proposed method. Similar to the conventional IMM algorithm, the proposed IMMAUKF consists of four major steps: mixing, filtering, calculating of each mode probability, and state fusion (Bar-Shalom et al, 1989). One cycle of this method is summarized as follows:…”

Section: The Immaukf Algorithmmentioning

confidence: 99%

“…The MM algorithms are developed due to the behavior of a target cannot be specified by a single model precisely. Among different MM techniques (Krishnamurthy and Evans, 1998;Li and Bar-Shalom, 1996;Bar-Shalom et al, 1989) the Interacting Multiple Model (IMM) algorithm is an interesting one to the excellent trade-off between complexity and performance (Mazor et al, 2000;Kim and Kim, 2007). This estimator includes the important areas of application such as: traffic forecasts (Zhang and Liu, 2009), Underwater Target Tracking (Xu et al, 2008), ground target tracking (Guo et al, 2008) and tactical ballistic missile tracking (Cooperman, 2002).…”

Section: The MM Approachmentioning

confidence: 99%

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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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Section: The Immaukf Algorithmmentioning

confidence: 99%

Section: The MM Approachmentioning

confidence: 99%

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

Amirzadeh¹,

Karimpour²,

Moeini³

2011

Sci. Res. Essays

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“…Another work that is akin to the proposed Frenet-Serret model (1)-(2) or equivalently (5) in state space form, is [8]. The proposed model is based on constant tangential and normal accelerations and constant-plane motion, and differs from [20], [1] only in the way the noise enters the system. The authors assume indeed the system to be deterministic, and the acceleration to jump from time to time.…”

Section: B Associated Difficulties and Links With Prior Literaturementioning

confidence: 99%

Tracking the Frenet-Serret frame associated to a highly maneuvering target in 3D

Pilté

Bonnabel

Barbaresco

2017

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

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Abstract-In this paper we consider the problem of tracking a maneuvering aircraft. The dynamical model used is based on assumptions of nearly constant tangential velocity, curvature, and torsion, of the target trajectory. Using the Frenet-Serret frame, that is, an element of the special orthogonal group SO(3) we cast the tracking problem into a filtering on Lie groups framework. We then use an invariant extended Kalman filter to estimate the various quantities involved. The resulting filter is simple to implement, and shown to gracefully accomodate some realistic target trajectories.

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“…Since the Singer's method, a lot of researches have been diversely developed. One approach is related to the input estimation (IE) technique [11][12][13][14][15] and the others are the interacting multiple model (IMM) and the adaptive interacting multiple model (AIMM) [16][17][18][19][20][21][22]. Later techniques provided the better tracking performance than prior one for a maneuvering target.…”

Section: Introductionmentioning

confidence: 99%

SIMM Method Based on Acceleration Extraction for Nonlinear Maneuvering Target Tracking

Son

Park²,

Joo

2012

Journal of Electrical Engineering and Technology

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-This paper presents the smart interacting multiple model (SIMM) using the concept of predicted point and maximum noise level. Maximum noise level means the largest value of the mere noises. We utilize the positional difference between measured point and predicted point as acceleration. Comparing this acceleration with the maximum noise level, we extract the acceleration to recognize the characteristics of the target. To estimate the acceleration, we propose an optional algorithm utilizing the proposed method and the Kalman filter (KF) selectively. Also, for increasing the effect of estimation, the weight given at each sub-filter of the interacting multiple model (IMM) structure is varying according to the rate of noise scale. All the procedures of the proposed algorithm can be implemented by an on-line system. Finally, an example is provided to show the effectiveness of the proposed algorithm.

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Tracking a maneuvering target using input estimation versus the interacting multiple model algorithm

Cited by 286 publications

References 16 publications

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

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

Tracking the Frenet-Serret frame associated to a highly maneuvering target in 3D

SIMM Method Based on Acceleration Extraction for Nonlinear Maneuvering Target Tracking

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