High Maneuver Target Tracking Based on Combined Kalman Filter and Fuzzy Logic

Bahari, Mohamad Hasan; Karsaz, Ali; Khaloozadeh, Hamid

doi:10.1109/idc.2007.374526

Cited by 19 publications

(22 citation statements)

References 10 publications

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“…On the other hand, if the observation residual or heading change is small, the target is weakly maneuvering with high probability. In addition, incorporation of them in tracking methods may lead to an immediate detection and a less delay about maneuvers [2,14,16]. Considering this fact, observation residuals and heading changes are usually combined to detect maneuvers.…”

Section: Analysis Of Maneuvering Target Motionsmentioning

confidence: 99%

“…Moreover, its performance depends on the assumptions on the basis sub-models. Two different modified Kalman filters proposed in [13] and [14] tend to extend the standard Kalman filter for MTT. Nonetheless, their computation is still expensive, and the proposed hybrid Kalman filter (HKF) in [14] is mainly applied to track an accelerating target.…”

Section: Introductionmentioning

confidence: 99%

“…Two different modified Kalman filters proposed in [13] and [14] tend to extend the standard Kalman filter for MTT. Nonetheless, their computation is still expensive, and the proposed hybrid Kalman filter (HKF) in [14] is mainly applied to track an accelerating target. To track a target making sharp turns or accelerating at nonuniform rates in stressful environments, the fuzzy gain α-β filter (FGα-βF) is proposed in [15].…”

Section: Introductionmentioning

confidence: 99%

“…To solve the problem, one of the most popular techniques has been applied in MTT. Fuzzy logic with intelligent adaptation capabilities has been widely used to relax or avoid the restrictive assumptions of motion models [12][13][14][15][16]. Due to the limitation of the standard IMM algorithm in real applications, the adaptive fuzzy IMM filter (AFIMMF) proposed in [12] defines several basis sub-models and time-varying mode transition probabilities to reduce its computational complexity.…”

Section: Introductionmentioning

confidence: 99%

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Maneuvering target tracking using fuzzy logic-based recursive least squares filter

Fan

Xie

Liu

2014

EURASIP J. Adv. Signal Process.

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In this paper, a fuzzy logic-based recursive least squares filter (FLRLSF) is presented for maneuvering target tracking (MTT) in situations of observations with unknown random characteristics. In the proposed filter, fuzzy logic is applied in the standard recursive least squares filter (RLSF) by the design of a set of fuzzy if-then rules. Given the observation residual and the heading change in the current prediction, these rules are used to determine the magnitude of the fading factor of RLSF. The proposed filter has an advantage in which the restrictive assumptions of statistical models for process noise, measurement noise, and motion models are relaxed. Moreover, it does not need a maneuver detector when tracking a maneuvering target. The performance of FLRLSF is evaluated by using a simulation and real test experiment, and it is found to be better than those of the traditional RLSF, the fuzzy adaptive α-β filter (FAα-βF), and the hybrid Kalman filter in tracking accuracy.

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Section: Analysis Of Maneuvering Target Motionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Maneuvering target tracking using fuzzy logic-based recursive least squares filter

Fan

Xie

Liu

2014

EURASIP J. Adv. Signal Process.

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“…Since target recognition usually outputs the possibility of target types, the output is fuzzy in nature. Furthermore, meteorology (such as airflow) influence the flight in a complicated way, which can be modeled by fuzzy relations [2,5,7]. Considering of the above situations, a novel fuzzy logic-based multi-factor aided multiple-model filter (FLMAMMF) is proposed which employs the above influencing factors to adjust TPM.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic-based multi-factor aided multiple-model filter for general aviation target tracking

Wang

Huang

Huang³

2015

IFS

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Abstract.A fuzzy logic-based multi-factor aided multiple-model filter (FLMAMMF) for General aviation (GA) maneuvering target tracking (MTT) is presented. The target category and meteorological information are introduced into the interacting multiple model (IMM) filter to perform GA target tracking. Fuzzy logic inference is employed in the proposed algorithm to reflect the complicated relationship between these two factors and the transition probability matrix (TPM). Both the number of models in model set and the transition probabilities between models are adjusted through fuzzy inference. Simulation results show that the proposed method is efficient and effective.

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An interacting Fuzzy-Fading-Memory-based Augmented Kalman Filtering method for maneuvering target tracking

Amirzadeh

Karimpour

2013

Digital Signal Processing

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High Maneuver Target Tracking Based on Combined Kalman Filter and Fuzzy Logic

Cited by 19 publications

References 10 publications

Maneuvering target tracking using fuzzy logic-based recursive least squares filter

Maneuvering target tracking using fuzzy logic-based recursive least squares filter

Fuzzy logic-based multi-factor aided multiple-model filter for general aviation target tracking

An interacting Fuzzy-Fading-Memory-based Augmented Kalman Filtering method for maneuvering target tracking

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