Vision Inertia Adaptive Fusion Method for Attitude Determination Based on Error State Kalman Filter
Helmet Mounted Sights(HMS)are auxiliary sighting equipment for new generation fighter pilots in recent years.They can help pilots enhance battlefield situational awareness and conduct rapid and precise strikes against enemy targets.The key to its normal operation is to obtain the attitude parameter of the pilot's head relative to the moving aircraft helmet-mounted sight.This paper investigates the key techniques for visual fusion and posture measurement in the context of helmet mounted sight.The visual inertial fusion method can realize the complementary advantages of these two target position measurement methods.However,the robustness and accuracy of the fusion algorithm need to be further improved,because the nominal noise matrix cannot be predicted absolutely and accurately.To address this problem,this paper proposes a visual inertial adaptive fusion method based on variational Bayesian inference in the error-state Kalman filter framework.First,the process noise is modeled using the inverse Wishart distribution.Then,the covariance is predicted in one step by introducing a latent variable,and the online estimation of the process noise covariance matrix is achieved by combining the variational Bayesian inference.Experimental findings unequivocally demonstrate that the proposed pose measurement algorithm exhibits remarkable accuracy and robustness in the face of complex motion and substantial deviations in the nominal noise covariance matrix.The proposed algorithm can complete fast and high-precision tracking of the target.
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