NDR-SHKF replaces the static forgetting factor in Sage-Husa Kalman Filters with a learned vector-valued memory attenuation policy from a bifurcated recurrent network trained end-to-end on whitened innovations to minimize estimation error.
Strapdown Inertial Navigation Technology
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
ALAC formulates accelerometer calibration as a constrained homogeneous least-squares problem on a combined error matrix, recovering scale, misalignment, and bias parameters from five attitude-aided measurements under static gravity.
A U-Net conditional diffusion model synthesizes virtual high-grade IMU data from low-cost measurements, yielding improved positioning and attitude estimates plus better airborne point clouds.
citing papers explorer
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Learned Memory Attenuation in Sage-Husa Kalman Filters for Robust UAV State Estimation
NDR-SHKF replaces the static forgetting factor in Sage-Husa Kalman Filters with a learned vector-valued memory attenuation policy from a bifurcated recurrent network trained end-to-end on whitened innovations to minimize estimation error.
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Attitude-Aided Linear Calibration of Triaxial Accelerometers
ALAC formulates accelerometer calibration as a constrained homogeneous least-squares problem on a combined error matrix, recovering scale, misalignment, and bias parameters from five attitude-aided measurements under static gravity.
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Overcoming the Intrinsic Performance Limitations of MEMS IMU via Diffusion-Based Generative Learning
A U-Net conditional diffusion model synthesizes virtual high-grade IMU data from low-cost measurements, yielding improved positioning and attitude estimates plus better airborne point clouds.