A Novel Adaptive Kalman Filter With Inaccurate Process and Measurement Noise Covariance Matrices

Huang, Yulong; Zhang, Yonggang; Wu, Zhen; Li, Ning; Chambers, Jonathon A.

doi:10.1109/tac.2017.2730480

Cited by 493 publications

(287 citation statements)

References 17 publications

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“…If τ is too small, the information of the process model will be also lost. According to the research result of [27], the optimal range of the turning parameter is τ ∈ [2,6], which has better estimation performance and estimation accuracy. The forgetting factor ξ also adjusts the influence ofR k−1 .…”

Section: The Proposed Adaptive Cubature Kalman Filtermentioning

confidence: 99%

An Improved SINS Alignment Method Based on Adaptive Cubature Kalman Filter

Zhang

Liu

2019

Sensors

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Initial alignment is critical and indispensable for the inertial navigation system (INS), which determines the initial attitude matrix between the reference navigation frame and the body frame. The conventional initial alignment methods based on the Kalman-like filter require an accurate noise covariance matrix of state and measurement to guarantee the high estimation accuracy. However, in a real-life practical environment, the uncertain noise covariance matrices are often induced by the motion of the carrier and external disturbance. To solve the problem of initial alignment with uncertain noise covariance matrices and a large initial misalignment angle in practical environment, an improved initial alignment method based on an adaptive cubature Kalman filter (ACKF) is proposed in this paper. By virtue of the idea of the variational Bayesian (VB) method, the system state, one step predicted error covariance matrix, and measurement noise covariance matrix of initial alignment are adaptively estimated together. Simulation and vehicle experiment results demonstrate that the proposed method can improve the accuracy of initial alignment compared with existing methods.

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Section: The Proposed Adaptive Cubature Kalman Filtermentioning

confidence: 99%

An Improved SINS Alignment Method Based on Adaptive Cubature Kalman Filter

Zhang

Liu

2019

Sensors

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“…The observation arcs are very short, usually only a few minutes, because of the limited field of view (FOV) of optical sensor and high relative angular velocities between observation platform and the space objects. There are many estimation algorithms that have been proposed for the orbit determination from a short-arc angle-only observation, such as genetic algorithms [8], batch [9], or sequential estimators [10][11][12]. However, in these algorithms, an efficient initial orbit determination (IOD) method is required to guarantee the final convergence, which is still not well solved.…”

Section: Introductionmentioning

confidence: 99%

Space-Based Optical Observations on Space Debris via Multipoint of View

Wang

Zheng

2020

International Journal of Aerospace Engineering

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In this paper, the space debris catalogue in LEO is fulfilled using multipoint of view (MPOV) observations constructed by a spacecraft formation. The dual-point of view (DPOV) observation is employed because it has higher cost-benefit ratio than other MPOV methods, and it required that the target is detectable for multiple optical sensors simultaneously. This condition is matched by designing the orbit of formation and the installation angles of sensors without frequent attitude maneuvers. The design parameters are optimized to maximize the matching degree of observation constraints to improve the coverage performance of the formation, and the equivalent ranging error is taken into account to ensure the orbit determination accuracy. Finally, the system capabilities of different schemes are compared by simulations. For the schemes with the same number of platforms, simulation results show that the initial orbit determination accuracy of the proposed scheme is higher.

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“…As a specific type of variational Bayes whereas the approximated PDF is assumed fully factorized, MF is also widely used in adaptive state estimation and target tracking problems. The MF approximation for adaptive Kalman filtering with unknown measurement noise covariance was presented in [33], which was further extended to both unknown process noise covariance and measurement noise covariance [34], and nonlinear adaptive filtering [35,36]. Ma et al [37] considered the multiple model state estimation problem, and approximated the joint state estimation and model identification through MF approximation.…”

Section: Introductionmentioning

confidence: 99%

A message passing approach for multiple maneuvering target tracking

Lan

Wang

et al. 2020

Signal Processing

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This paper considers the problem of detecting and tracking multiple maneuvering targets, which suffers from the intractable inference of high-dimensional latent variables that include target kinematic state, target visibility state, motion mode-model association, and data association. A unified message passing algorithm that combines belief propagation (BP) and mean-field (MF) approximation is proposed for simplifying the intractable inference. By assuming conjugate-exponential priors for target kinematic state, target visibility state, and motion mode-model association, the MF approximation decouples the joint inference of target kinematic state, target visibility state, motion mode-model association into individual low-dimensional inference, yielding simple message passing update equations. The BP is exploited to approximate the probabilities of data association events since it is compatible with hard constraints. Finally, the approximate posterior probability distributions are updated iteratively in a closed-loop manner, which is effective for dealing with the coupling issue between the estimations of target kinematic state and target visibility state and decisions on motion mode-model association and data association. The performance of the proposed algorithm is demonstrated by comparing with the well-known multiple maneuvering target tracking algorithms, including interacting multiple model joint probabilistic data association, interacting multiple model hypothesis-oriented multiple hypothesis tracker and multiple model generalized labeled multi-Bernoulli.

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A Novel Adaptive Kalman Filter With Inaccurate Process and Measurement Noise Covariance Matrices

Cited by 493 publications

References 17 publications

An Improved SINS Alignment Method Based on Adaptive Cubature Kalman Filter

An Improved SINS Alignment Method Based on Adaptive Cubature Kalman Filter

Space-Based Optical Observations on Space Debris via Multipoint of View

A message passing approach for multiple maneuvering target tracking

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