Attitude estimation based on recurrent neural network and vector observations for attitude and heading reference system

Li, Xiang; Xiao-qin, Liu; Yaohua, Liu

doi:10.1109/icemi46757.2019.9101833

Cited by 1 publication

(1 citation statement)

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“…A method for training deep neural networks was developed in [21] for enhancing the AE by a Kalman filter. In [22] a novel attitude estimator model was conceived based on recurrent neural networks able to eliminate sensor errors, while implementing dynamic AE. A long short-term memory neural network was trained in [23] using real quadrotor sensors' data for AE, and high accuracy results were obtained.…”

Section: Introductionmentioning

confidence: 99%

Learning-Based Mitigation of Soft Error Effects on Quaternion Kalman Filter Processing

Kraemer Sarzi Sartori,

Fourati,

Bastos

2024

IEEE Sensors J.

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This paper proposes a novel mitigation technique for soft error effects on the attitude estimation (AE) processing for spacecrafts, especially for satellites' application. Specially, we are focused on the soft errors that occur in space and affect, for example, the quaternion Kalman filter, running on the processor of control system of satellite, which leads to invert bits of the estimated states, miscalculations and a decreased performance. The mitigation technique detects first the presence of soft error effects on the AE algorithm output using some residuals. Then the residuals are passed to a trained Machine Learning (ML) models to estimate the quaternion error that will be used to correct the estimations. A supervised regression solution was proposed to correct the soft error effects, in which a methodology for creating a dataset for training classical ML models was developed. The results from the case-study scenario show a high reduction of soft error effects, while adding little overhead to the Kalman filter processing.

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Section: Introductionmentioning

confidence: 99%