Square-Root Cubature Kalman Filter Based on H∞ Filter for Attitude Measurement of High-Spinning Aircraft

Zhang, Pingan; Wang, Wei; Gao, Min; Wang, Yi

doi:10.1155/2021/5589691

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…The purpose of the H-infinity filter is to estimate

minimized

. In general, the analytical solution of the optimal H-infinity filter problem is challenging to obtain, so the suboptimal solution is generally sought [ 25 ]. In the worst case, the boundary of

satisfies:

where sup represents the upper bound and

is a positive scalar parameter that limits the estimation error of the system due to uncertainty.…”

Section: The H-infinity Cubature Kalman Filtermentioning

confidence: 99%

See 1 more Smart Citation

A Novel Adaptive Robust Cubature Kalman Filter for Maneuvering Target Tracking with Model Uncertainty and Abnormal Measurement Noises

Wang

et al. 2023

Sensors

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The features of measurement and process noise are directly related to the optimal performance of the cubature Kalman filter. The maneuvering target model’s high level of uncertainty and non-Gaussian mean noise are typical issues that the radar tracking system must deal with, making it impossible to obtain the appropriate estimation. How to strike a compromise between high robustness and estimation accuracy while designing filters has always been challenging. The H-infinity filter is a widely used robust algorithm. Based on the H-infinity cubature Kalman filter (HCKF), a novel adaptive robust cubature Kalman filter (ARCKF) is suggested in this paper. There are two adaptable components in the algorithm. First, an adaptive fading factor addresses the model uncertainty issue brought on by the target’s maneuvering turn. Second, an improved Sage–Husa estimation based on the Mahalanobis distance (MD) is suggested to estimate the measurement noise covariance matrix adaptively. The new approach significantly increases the robustness and estimation precision of the HCKF. According to the simulation results, the suggested algorithm is more effective than the conventional HCKF at handling system model errors and abnormal observations.

show abstract

“…The purpose of the H-infinity filter is to estimate

minimized

. In general, the analytical solution of the optimal H-infinity filter problem is challenging to obtain, so the suboptimal solution is generally sought [ 25 ]. In the worst case, the boundary of

satisfies:

where sup represents the upper bound and

is a positive scalar parameter that limits the estimation error of the system due to uncertainty.…”

Section: The H-infinity Cubature Kalman Filtermentioning

confidence: 99%

“…The purpose of the H-infinity filter is to estimate xk minimized J ∞ . In general, the analytical solution of the optimal H-infinity filter problem is challenging to obtain, so the suboptimal solution is generally sought [25]. In the worst case, the boundary of J ∞ satisfies:…”

Section: The H-infinity Cubature Kalman Filtermentioning

confidence: 99%