A Hybrid Model Algorithm for Hypersonic Glide Vehicle Maneuver Tracking Based on the Aerodynamic Model

Fan, Yu Gang; Lu, Fang; Zhu, Wen-Hong; Bai, Guangzhou; Liang, Yan

doi:10.3390/app7020159

Cited by 14 publications

(15 citation statements)

References 12 publications

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“…The other gains are expressed using tracking indices (see Equations (24)- (27) of [31]). However, this filter is not optimal for other models, such as the frequently-used random-velocity model [9] and the diagonal Q, which does not include correlations in process noise [1,2]. Other process noise can be incorporated using arbitrary process noise; see [4].…”

Section: Optimal Filter For a Random-acceleration Model And Its Problemsmentioning

confidence: 99%

“…For example, substituting (a, b, c) = (qT 4 /4, qT 3 /2, qT 2 ) into Equation (33) gives the Q ra of (16); substituting (a, b, c) = (q v T 2 , q v T, q v ) (q v is the variance of the velocity noise) yields the random-velocity model [9]; and b = 0 leads to a diagonal Q, which is also a well-used setting in real applications [1,2]. The relationship between steady state Kalman gains and Q gen is derived as:…”

Section: Relationship With Steady State Pvm Kalman Filtersmentioning

confidence: 99%

“…Adaptive tracking techniques such as Kalman and extended Kalman filters [1][2][3][4][5] and particle filters [6,7] are commonly used for this purpose because of their accuracy. An alternative option is fixed-gain tracking filters, which have also been studied and used extensively for two reasons [8][9][10][11][12]:…”

Section: Introductionmentioning

confidence: 99%

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Performance Analysis and Design Strategy for a Second-Order, Fixed-Gain, Position-Velocity-Measured (α-β-η-θ) Tracking Filter

Saho

Masugi

2017

Applied Sciences

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Featured Application: Design and evaluation of monitoring systems in intelligent vehicles, robots, and so on.Abstract: We present a strategy for designing an α-β-η-θ filter, a fixed-gain moving-object tracking filter using position and velocity measurements. First, performance indices and stability conditions for the filter are analytically derived. Then, an optimal gain design strategy using these results is proposed and its relationship to the position-velocity-measured (PVM) Kalman filter is shown. Numerical analyses demonstrate the effectiveness of the proposed strategy, as well as a performance improvement over the traditional position-only-measured α-β filter. Moreover, we apply an α-β-η-θ filter designed using this strategy to ultra-wideband Doppler radar tracking in numerical simulations. We verify that the proposed strategy can easily design the gains for an α-β-η-θ filter based on the performance of the ultra-wideband Doppler radar and a rough approximation of the target's acceleration. Moreover, its effectiveness in predicting the steady state performance in designing the position-velocity-measured Kalman filter is also demonstrated.

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Section: Optimal Filter For a Random-acceleration Model And Its Problemsmentioning

confidence: 99%

Section: Relationship With Steady State Pvm Kalman Filtersmentioning

confidence: 99%

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Performance Analysis and Design Strategy for a Second-Order, Fixed-Gain, Position-Velocity-Measured (α-β-η-θ) Tracking Filter

Saho

Masugi

2017

Applied Sciences

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“…Therefore, for the internal IMM-UKF link, the filtering problem to be solved is how to design an effective adaptive UKF algorithm for systems given by Equation (2), when the sensor fault δh out,k exists.…”

Section: Sensor Faultsmentioning

confidence: 99%

“…With the continuous development of space technology, maneuvering ability in real time is a key factor in completing complicated space missions for maneuvering targets [1][2][3][4]. Performing accurate maneuvering target tracking and precise locating are the core issues in the field of space target surveillance [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A Cubature-Principle-Assisted IMM-Adaptive UKF Algorithm for Maneuvering Target Tracking Caused by Sensor Faults

et al. 2017

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Featured Application: The work is very effective and applicable in the fields of the maneuvering target tracking and navigations system design.Abstract: Aimed at solving the problem of decreased filtering precision while maneuvering target tracking caused by non-Gaussian distribution and sensor faults, we developed an efficient interacting multiple model-unscented Kalman filter (IMM-UKF) algorithm. By dividing the IMM-UKF into two links, the algorithm introduces the cubature principle to approximate the probability density of the random variable, after the interaction, by considering the external link of IMM-UKF, which constitutes the cubature-principle-assisted IMM method (CPIMM) for solving the non-Gaussian problem, and leads to an adaptive matrix to balance the contribution of the state. The algorithm provides filtering solutions by considering the internal link of IMM-UKF, which is called a new adaptive UKF algorithm (NAUKF) to address sensor faults. The proposed CPIMM-NAUKF is evaluated in a numerical simulation and two practical experiments including one navigation experiment and one maneuvering target tracking experiment. The simulation and experiment results show that the proposed CPIMM-NAUKF has greater filtering precision and faster convergence than the existing IMM-UKF. The proposed algorithm achieves a very good tracking performance, and will be effective and applicable in the field of maneuvering target tracking.

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An adaptive multivariate Student's t‐process recursive method for hypersonic glide vehicle trajectory prediction

Xue

Huang

Wei

2023

IET Radar Sonar & Navi

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The trajectory prediction of the hypersonic glide vehicle (HGV) can provide hit point information for early warning systems, which is of great significance for near‐space defence operations. However, the heavy‐tailed noise caused by abnormal environmental disturbance and abrupt changes in vehicle trajectory seriously affects the accuracy of HGV trajectory prediction. To solve the problem of trajectory prediction for HGV under heavy‐tailed noise, we propose an adaptive multivariate Student's t‐process regression method called aEM‐MVTPR. Firstly, a heavy‐tailed noise model is developed using the Student's t‐distribution. Secondly, a multivariate Student's t‐process regression (MVTPR) for HGV trajectory prediction is derived, and the method can learn the HGV trajectory time series features and mine the correlations among the trajectory variables. Finally, to further improve the method's robustness, we use the accelerated expectation maximisation algorithm and Pearson correlation analysis to adaptively estimate and adjust the initial values of the MVTPR parameters. The simulation experimental results show that the proposed method has more accurate predicted values of trajectory variables than multivariate Gaussian process regression (MVGPR) under heavy‐tailed noise conditions. In addition, the ability of the aEM‐MVTPR to adaptively adjust the parameter makes it more robust than the MVTPR under different noise environments.

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A Hybrid Model Algorithm for Hypersonic Glide Vehicle Maneuver Tracking Based on the Aerodynamic Model

Cited by 14 publications

References 12 publications

Performance Analysis and Design Strategy for a Second-Order, Fixed-Gain, Position-Velocity-Measured (α-β-η-θ) Tracking Filter

Performance Analysis and Design Strategy for a Second-Order, Fixed-Gain, Position-Velocity-Measured (α-β-η-θ) Tracking Filter

A Cubature-Principle-Assisted IMM-Adaptive UKF Algorithm for Maneuvering Target Tracking Caused by Sensor Faults

An adaptive multivariate Student's t‐process recursive method for hypersonic glide vehicle trajectory prediction

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