Adaptive Interacting Multiple Model Algorithm Based on Information-Weighted Consensus for Maneuvering Target Tracking

Ding, Ziran; Liu, Yu; Li, Jun; Yu, Kaimin; You, Yuanyang; Jing, Peiliang; He, Yong

doi:10.3390/s18072012

Cited by 15 publications

(13 citation statements)

References 31 publications

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“…Equation (12) shows that the mode space of the vehicle is determined by three characteristic parameters. The modelset design method should be aimed at approximating the mode space effectively.…”

Section: A Analysis Of Characteristic Parametersmentioning

confidence: 99%

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Multiple Model Tracking for Hypersonic Gliding Vehicles With Aerodynamic Modeling and Analysis

Lei

Shao

et al. 2019

IEEE Access

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The multiple model tracking methods for hypersonic gliding vehicles (HGVs), which improves the design of tracking model-set is proposed in this paper. Different from the traditional tracking method with dynamic pressure models, the proposed method refines the model-set by modeling and analyzing the specific aerodynamic characteristics of the HGVs. Based on the aerodynamic model of the vehicle, the novel tracking models are established and the multiple model tracking methods are adopted. The uniform modelsets are designed by determining the representative points from the constrained characteristic parameters to ensure the stability of the tracking system. Moreover, the model-sets are then augmented by the real-time updates of the motion estimations to further improve the tracking accuracy. The simulation studies indicate that the proposed tracking method outperforms the method with dynamic pressure models in both position and velocity estimations, the position estimation accuracy is increased by about 23%-53% and the velocity estimation accuracy is increased by about 42%-56% in the given examples. INDEX TERMS Hypersonic gliding vehicle, aerodynamic modeling, multiple model tracking, model-set design.

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Section: A Analysis Of Characteristic Parametersmentioning

confidence: 99%

“…With sufficient details of the dynamic characteristics, the dynamic models can achieve a better accuracy than the kinematic models. To further improve the tracking performance, the multiple model methods have been widely used [10]- [12], where multiple models are utilized simultaneously to estimate one motion mode.…”

Section: Introductionmentioning

confidence: 99%

Multiple Model Tracking for Hypersonic Gliding Vehicles With Aerodynamic Modeling and Analysis

Lei

Shao

et al. 2019

IEEE Access

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“…As a result, the estimation accuracy was higher than that in [8]. The form of Singer acceleration model [25] is as follows:…”

Section: Casementioning

confidence: 98%

Improved Student’s t-Based Unscented Filter and its Application to Trajectory Estimation for Maneuvering Target

2019

Applied Sciences

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To estimate the systems with one-step randomly delayed measurement and time-correlated heavy-tailed measurement noises, on the basis of robust Student’s t based unscented filter (RSTUF), an improved Student’s t based unscented filter (ISTUF) is proposed. Referring to the measurement differencing method, a reformed measurement model was built. Then, by augmenting the system state vector, the conditional probability distribution of the measurement noise with respect to delayed measurement was taken into consideration. Based on the reformed measurement model and the augment state vector, a novel estimator was designed to solve the one-step randomly delayed problem. Maneuvering target tracking systems were used for simulation. Compared with unscented Kalman filter (UKF) or RSTUF, ISTUF had higher accuracy.

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“…Let be the consensus matrix, whose elements are π i,j for any i, j ∈ S. Assume the initial value of node i ∈ S is α 0 i . If is row stochastic and primitive [1]- [5], [10], [23], [26], [32]- [34], the value α l i will converge to the mean of the initial value as l → +∞, i.e., lim…”

Section: Distributed High-degree Cubature Information Filter Withmentioning

confidence: 99%

Distributed High-Degree Cubature Information Filter With Embedded Hybrid Consensus

Liu

Dong

et al. 2019

IEEE Access

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To settle the problem of distributed nonlinear state estimation in sensor networks with naive nodes, a novel distributed high-degree cubature information filter with embedded hybrid consensus (DHCIF) is proposed. The multi-dimensional Gaussian weighted integrals involved in the filtering process are approximated by the fifth-degree cubature rule. A novel scheme, with consideration for the predicted measurement errors, is applied to compute the information contribution. With parallel consensus performed on both prior and measurement information, the proposed DHCIF is derived. The stability analysis with regard to consistency and boundedness of estimation errors for the proposed DHCIF is also developed. Finally, the effectiveness and advantage of the proposed DHCIF is validated by a typical maneuvering target tracing scenario. The experimental results indicate that the proposed DHCIF outperforms the existing algorithms in the aspects of estimation accuracy, consistency and consensus at the cost of a little more extra computation burden.

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Adaptive Interacting Multiple Model Algorithm Based on Information-Weighted Consensus for Maneuvering Target Tracking

Cited by 15 publications

References 31 publications

Multiple Model Tracking for Hypersonic Gliding Vehicles With Aerodynamic Modeling and Analysis

Multiple Model Tracking for Hypersonic Gliding Vehicles With Aerodynamic Modeling and Analysis

Improved Student’s t-Based Unscented Filter and its Application to Trajectory Estimation for Maneuvering Target

Distributed High-Degree Cubature Information Filter With Embedded Hybrid Consensus

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