A new adaptive Kalman filter for navigation systems of carrier-based aircraft

Zhang, Lifei; Wang, Shaoping; Selezneva, M. S.; Neusypin, K. A.

doi:10.1016/j.cja.2021.04.014

Cited by 29 publications

(13 citation statements)

References 18 publications

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“…In Equation (10), dm is the mass element of the total mass of the earth. The distance between the mass of a particle outside the earth and dm is ρ.…”

Section: Algorithm Designmentioning

confidence: 99%

“…The third is that the navigation system model of aerospace vehicle is inconsistent with the filter model due to large dynamic flight, which leads to the decline of filter estimation accuracy. In recent years, researchers have solved the problem that the accuracy of integrated navigation system decreases due to the inaccurate error model through the research of the adaptive filter [9,10] and IMM filter algorithm [11]. According to the accurate modelling of aerospace vehicle navigation system, it can provide high-precision state quantity and measurement quantity for the subsequent filtering algorithm and then improve the accuracy of navigation parameter estimation.…”

Section: Introductionmentioning

confidence: 99%

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Integrated Navigation Method of Aerospace Vehicle Based on Rank Statistics

Kang

Xiong

Zhang

2023

International Journal of Aerospace Engineering

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The large dynamic and high-speed flight of aerospace vehicle will bring unpredictable conditions to its navigation system, resulting in that its system random noise probability distribution will no longer meet the preconditions of Gaussian distribution preset by the existing filter algorithm, thus reducing the accuracy of the navigation system. So, it is very important to propose an effective method to solve the filter problem of the navigation system in non-Gaussian distribution to improve the accuracy of the navigation system. Therefore, an integrated navigation method of aerospace vehicle based on rank statistics (LRF) has been proposed in this paper. Firstly, based on the flight characteristics of aerospace vehicles, an accurate gravity calculation model has been established to improve the accuracy of system modelling. Then, the state equation and measurement equation of integrated navigation system have been established. In combination with the rank filter algorithm as well as the determined weights, sampling points are calculated and nonlinearly propagated through the transition matrix to achieve an accurate estimation about the predicted values of the state quantities and measurement quantities and the covariance matrix. In turn, it simulates the probability distribution of the system state effectively. Therefore, when the system random noise probability distribution of the aerospace vehicle does not meet the Gaussian distribution due to various interference factors in the actual flight process, the algorithm can simulate the probability distribution of the actual system to the greatest extent, to improve the accuracy of the integrated navigation system and enhance the reliability of the navigation system ultimately.

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“…In Equation (10), dm is the mass element of the total mass of the earth. The distance between the mass of a particle outside the earth and dm is ρ.…”

Section: Algorithm Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrated Navigation Method of Aerospace Vehicle Based on Rank Statistics

Kang

Xiong

Zhang

2023

International Journal of Aerospace Engineering

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“…Research is mainly focused on actuator faults (Kopecki, 2021; Liu et al , 2022), sensor faults (Unal, 2021) and structural faults (Wang et al , 2016). Very little research studies the fault-tolerant control for UAV automatic carrier landing; most people are paying close attention to the fault-tolerant control for spacecraft (Lu et al , 2022), UAV attitude tracking (Yu et al , 2022) and robotic manipulators (Zhang et al , 2022a, 2022b). Some methods, for example, use the adaptive fault-tolerant control to make the control gain adapt to the uncertainties caused by faults (Xue et al , 2020), which can only reduce the impact of faults with limited capability.…”

Section: Introductionmentioning

confidence: 99%

“…The characteristics, such as low cost, no casualties and tireless, make UAVs more competitive over manned aerial vehicles. The fixed-wing UAV has strict requirements for the landing desk and control system, and it would be more likely to cause accidents during the landing phase (Zhang et al, 2022a(Zhang et al, , 2022b. Therefore, the automatic carrier landing system (ACLS), which is robust to external disturbances and actuator faults, is brought into focus.…”

Section: Introductionmentioning

confidence: 99%

Automatic carrier landing for UAV based on integrated disturbance observer and fault-tolerant control

Xue

Zhen

Zhang

et al. 2023

AEAT

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Purpose Accurate glide path tracking is vital to the automatic carrier landing task of unmanned aerial vehicle (UAV). The purpose of this paper is to develop a reliable flight controller that can simultaneously deal with external disturbance, structure fault and actuator fault. Design/methodology/approach The automatic carrier landing task is resolved into the glide path tracking problem and attitude tracking problem. The disturbance observer-based adaptive sliding mode control scheme is proposed for system stabilization, disturbance rejection and fault tolerance. Findings Both the Lyapunov method and exemplary simulations can prove that the disturbance estimation error and the attitude tracking error converge in finite time in the presence of external disturbances and various faults. Practical implications The presented algorithm is testified by a UAV automatic carrier landing simulation, which shows the potential of practical usage. Originality/value The barrier function is introduced to adaptively update both the sliding mode observer gain and sliding mode controller gain, so that the sliding mode surface could converge to a predefined region without overestimation. The proposed flight controller ensures a secure carrier landing task.

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“…To make the Kalman filter stable and non-divergent, some scholars have combined GPS with an inertial navigation system (INS) and applied it to an adaptive Kalman filter. In addition, some scholars have studied two different adaptive Kalman filters for vehicle navigation based on predictive residuals, one based on fading memory (Hu et al 2003) and the other based on variance estimation (Zhang et al 2022). The positioning accuracy of the adaptive Kalman filter realized using the two methods is better than that of a traditional Kalman filter, especially when the vehicle is turning (Zhang et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Kalman filter based on integer ambiguity validation in moving base RTK

et al. 2022

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In high-precision dynamic positioning, it is necessary to ensure the positioning accuracy and reliability of the navigation system, especially for safety–critical applications, such as intelligent vehicle navigation. In the face of a complex observation environment, when the global navigation satellite system (GNSS) uses carrier phase observations for high-precision relative positioning, ambiguity resolution will be affected, and it is difficult to estimate all ambiguities. In addition, when the GNSS signal quality and measurement noise level are difficult to predict in an environment with many occlusions, the received satellite observations are prone to very large errors, resulting in apparent deviations in the positioning solution. However, traditional positioning algorithms assume that the measurement noise is constant, which is unrealistic. This will cause incorrect ambiguity resolution, lead to meter-level positioning errors, reduce the reliability of the system, and increase the integrity risk of the system. We proposed an innovative adaptive Kalman filter based on integer ambiguity validation (IAVAKF) to improve the efficiency of ambiguity resolution (AR) and positioning accuracy. The partial ambiguity resolution (PAR) method is applied to solve the integer ambiguities. Then, the accuracy of the fixed ambiguity is verified by the ambiguity success rate. Taking the ambiguity success rate as a dynamic adjustment factor, the measurement noise matrix and variance–covariance matrix of the state estimation is adaptively adjusted at each time interval in the Kalman filter to provide a smoothing effect for filtering. The optimal Kalman filter gain matrix is obtained to improve positioning accuracy and reliability. As a result, the static and dynamic vehicle experiments show that the positioning accuracy of the proposed IAVAKF is improved by 26% compared with the KF. Through the IAVAKF, a more realistic PL can be obtained and applied to evaluate the integrity of the navigation system in the position domain. It can reduce the false alarm rate by 2.45% and 1.85% in the horizontal and vertical directions, respectively.

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A new adaptive Kalman filter for navigation systems of carrier-based aircraft

Cited by 29 publications

References 18 publications

Integrated Navigation Method of Aerospace Vehicle Based on Rank Statistics

Integrated Navigation Method of Aerospace Vehicle Based on Rank Statistics

Automatic carrier landing for UAV based on integrated disturbance observer and fault-tolerant control

Adaptive Kalman filter based on integer ambiguity validation in moving base RTK

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