Improved diagonal interacting multiple model algorithm for manoeuvering target tracking based on
            <i>H</i>
            <sub>∞</sub>
            filter

Fu, Xiouhua; Shang, Yuanyuan; Yuan, Honglin

doi:10.1049/iet-cta.2014.0685

Cited by 14 publications

(2 citation statements)

References 18 publications

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“…Like the regularized robust state estimator was developed to modify parameters using an estimated parameter [12]. The interacting multiple model (IMM) state estimator [13], which utilizes the multiple motion models for estimation, can also be considered as a type of robust state estimator and has been widely applied in fields such as vehicle driving [14], manoeuvering target tracking [15], and event-based system [16].…”

Section: Introductionmentioning

confidence: 99%

Multiple adaptive factors based interacting multiple model estimator

Sun,

Duan,

Xia

et al. 2024

IET Control Theory & Appl

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In the field of optoelectronic tracking, precisely modeling the motion equations of the tracked target is often challenging, and in some cases, they may even be entirely unknown. This necessitates the use of a robust state estimator for accurate state estimation. Additionally, atmospheric turbulence, variations in illumination, and intricate observation backgrounds may introduce a significant increase in observation noise for the tracked target. To address these challenges, one approach is to introduce adaptive factors, such as the Mahalanobis method, into the robust state estimator to enhance estimation accuracy. However, further exploration has revealed that adaptive factors designed using different methods offer unique advantages in scenarios with varying levels of noise amplification. In this paper, different adaptive factors are further combined using an interacting multiple model approach, allowing the designed state estimator to exhibit stronger adaptability to noise amplification. The stability and effectiveness of this algorithm are validated through program simulations, double reflection mirror experiment, and drone trace prediction, demonstrating its applicability and reliability in diverse scenarios.

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Section: Introductionmentioning

confidence: 99%

Multiple adaptive factors based interacting multiple model estimator

Sun,

Duan,

Xia

et al. 2024

IET Control Theory & Appl

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“…These new approaches weigh differently when mixing beginning model values with states and corresponding covariances at each step of the IMM algorithm. Fu et al also introduced H∞ filtering into the distributed interacting multiple model (DIMM) algorithm instead of KF, for target tracking of maneuvering during which the measurement noise is statistically unknown [23]. Park et al propose a new algorithm suitable for multi-object tracking using multi-data fusion by applying centralized KF to a typical IMM.…”

Section: Introductionmentioning

confidence: 99%

An Interacting Multiple Model Approach for Target Intent Estimation at Urban Intersection for Application to Automated Driving Vehicle

Shin

Park

et al. 2020

Applied Sciences

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Research shows that urban intersections are a hotspot for traffic accidents which cause major human injuries. Predicting turning, passing, and stop maneuvers against surrounding vehicles is considered to be fundamental for advanced driver assistance systems (ADAS), or automated driving systems in urban intersections. In order to estimate the target intent in such situations, an interacting multiple model (IMM)-based intersection-target-intent estimation algorithm is proposed. A driver model is developed to represent the driver’s maneuvering on the intersection using an IMM-based target intent classification algorithm. The performance of the intersection-target-intent estimation algorithm is examined through simulation studies. It is demonstrated that the intention of a target vehicle is successfully predicted based on observations at an individual intersection by proposed algorithms.

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Mathematical modeling and control of DFIG-based wind energy system by using optimized linear quadratic regulator weight matrices

Bhushan

Chatterjee

2017

Int Trans Electr Energ Syst

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Summary This paper deals with a genetic algorithm (GA)–based linear quadratic regulator (LQR) controller to improve the dynamic response, stability, and robustness of the doubly fed induction generator (DFIG) system at various stator voltage disturbances. The complete model has been represented by a state‐space model. This helps to optimally control all the states through the full‐state feedback LQR controller. GA is employed in the LQR algorithm for optimal tuning of the Q and R matrices. For finding out the effectiveness of the proposed controller, its dynamic response has been compared with proportional integral (PI) and LQR controllers. The simulation results indicate that the proposed controller has better performance in comparison to the PI and LQR controllers in terms of peak value, settling time, rise time, and steady‐state values of the DFIG outputs. The stability and robustness of the study system have also been investigated by the eigenvalue and participation factor analyses.

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Improved diagonal interacting multiple model algorithm for manoeuvering target tracking based on H _∞ filter

Cited by 14 publications

References 18 publications

Multiple adaptive factors based interacting multiple model estimator

Multiple adaptive factors based interacting multiple model estimator

An Interacting Multiple Model Approach for Target Intent Estimation at Urban Intersection for Application to Automated Driving Vehicle

Mathematical modeling and control of DFIG-based wind energy system by using optimized linear quadratic regulator weight matrices

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Improved diagonal interacting multiple model algorithm for manoeuvering target tracking based on H ∞ filter

Cited by 14 publications

References 18 publications

Multiple adaptive factors based interacting multiple model estimator

Multiple adaptive factors based interacting multiple model estimator

An Interacting Multiple Model Approach for Target Intent Estimation at Urban Intersection for Application to Automated Driving Vehicle

Mathematical modeling and control of DFIG-based wind energy system by using optimized linear quadratic regulator weight matrices

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Improved diagonal interacting multiple model algorithm for manoeuvering target tracking based on H _∞ filter