Space infrared tracking of a hypersonic cruise vehicle using an adaptive scaling UKF

Liu, Jiahui; Luo, Qi; Lou, Jiaxin; Li, Yuankai

doi:10.1007/s42401-020-00061-y

Cited by 4 publications

(2 citation statements)

References 11 publications

(13 reference statements)

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“…Target tracking refers to the use of various sensors, such as infrared, radar, and laser, to detect moving targets and deal with their echoes to obtain state information, such as target position and velocity. With the development of modern aviation, navigation, and aerospace industries, target tracking has received increasing attention and has become a very active research field (Liu et al, 2020; Wu et al, 2021). The target tracking has been widely applied in both civilian and military fields.…”

Section: Introductionmentioning

confidence: 99%

A novel adaptive maximum correntropy cubature Kalman filter based on multiple fading factors

Gu,

Jing,

2024

Transactions of the Institute of Measurement and Control

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In this paper, an adaptive maximum correntropy cubature Kalman filter based on multiple fading factors (MAMCKF) is proposed to address the problem of inaccurate process noise covariance and unknown measurement noise covariance together with outliers in target tracking. Although there are many adaptive filters and robust filters have been proposed to handle unknown measurement noise covariance or measurement outliers, most filters cannot deal with both unknown noise covariance and outliers simultaneously. In this article, we propose an adaptive and robust cubature Kalman filter. The modified measurement noise covariance matrix (MNCM) and innovation covariance matrix are used to construct multiple fading factors for correcting the prediction error covariance matrix (PECM), which can achieve adaptability. Then, the maximum correntropy criterion (MCC) is introduced to suppress outliers, which further enhances the robustness. Compared with the existing approaches, the proposed approach improves the performance by at least 5% in unknown time-varying noise, unknown time-varying heavy-tailed noise, and non-Gaussian heavy-tailed noise scenarios. The simulation results show that the proposed approach can effectively suppress inaccurate process noise covariance and unknown time-varying measurement noise together with outliers. Compared with the existing filtering approaches, the proposed approach exhibits both adaptability and robustness.

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Section: Introductionmentioning

confidence: 99%

A novel adaptive maximum correntropy cubature Kalman filter based on multiple fading factors

Gu,

Jing,

2024

Transactions of the Institute of Measurement and Control

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“…However, it is prone to divergence problems in situations where nonlinearity is strong and the amount of data is limited. Therefore, the Unscented Kalman Filter (UKF) [15,16] and the Cubature Kalman Filter (CKF) [17,18] have been proposed. Huang et al introduced the variance statistical function in UKF to calculate the residual weight in real time, which improved the sensitivity of the system to target maneuvering [19].…”

Section: Introductionmentioning

confidence: 99%

Velocity Estimation for Space Infrared Dim Targets Based on Multi-Satellite Observation and Robust Locally Weighted Regression

Zhang,

Rao,

Zhang

et al. 2023

Remote Sensing

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Velocity estimation of space moving targets is a key part of space situational awareness. However, most of the existing methods do not consider the satellite observation process, and the performance mainly depends on the preset target motion state, which has great limitations. To accurately obtain the motion characteristics of space infrared dim targets in space-based infrared detection, a velocity estimation method based on multi-satellite observation and robust locally weighted regression is proposed. Firstly, according to parameters such as satellite position, satellite attitude angle, and sensor line of sight, the overall target observation model from the sensor coordinate frame to the Earth-centered inertial coordinate frame is established, and the pixel coordinates of the target imaging point are extracted using the gray-weighted centroid method. Then, combined with the least squares criterion, the position sequence of the space target is obtained. Finally, a robust locally weighted regression operation is performed on the target position sequence to estimate the velocity. This study verified the feasibility of the proposed method through simulation examples, with the results showing that the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of the method were only 0.0733 m/s and 1.6640 m/s without measurement error. Moreover, the velocity estimation accuracy was better than that of other methods in most scenarios. In addition, the estimation accuracy under the impact of various measurement errors was analyzed, and it was found that the pixel coordinate extraction error had the greatest impact on velocity estimation accuracy. The proposed method provides a technical basis for the recognition of space infrared dim moving targets.

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Intelligent sensing and control for aerospace unmanned systems

Liu

Guo

et al. 2022

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Space infrared tracking of a hypersonic cruise vehicle using an adaptive scaling UKF

Cited by 4 publications

References 11 publications

A novel adaptive maximum correntropy cubature Kalman filter based on multiple fading factors

A novel adaptive maximum correntropy cubature Kalman filter based on multiple fading factors

Velocity Estimation for Space Infrared Dim Targets Based on Multi-Satellite Observation and Robust Locally Weighted Regression

Intelligent sensing and control for aerospace unmanned systems

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