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Deep-Learning-Assisted Kalman Filter for GNSS/MEMS IMU Integration in GNSS-Denied Environments
Item type: Journal Article
Li S.; Mikhaylov M.; Pany T.; et al. (2026)
This study introduces a novel deep-learning-assisted Kalman filter (DLKF) for integrating global navigation satellite system (GNSS) and inertial navigation system (INS) measurements, aiming to improve navigation accuracy and robustness in GNSS-denied environments. GNSS/INS integration is critical for autonomous vehicles to maintain continuous navigation when GNSS signals are obstructed. Traditional model-based Kalman filters (MBKFs), while widely employed for GNSS/INS integration, face challenges such as system model deficiencies, incorrect noise assumptions, and complex inertial measurement unit (IMU) error characteristics, especially for micro-electromechanical system IMUs. To address these challenges, the DLKF integrates a deep neural network (DNN) with the Kalman filter, leveraging its ability to learn non-linear system dynamics and noise statistics to provide adaptive Kalman gain and accurate IMU error estimation. Our approach further addresses practical challenges such as training with asynchronous sensor data and adapting to an error-state MBKF structure. This method also leverages DNN capabilities while retaining the interpretability of model-based filtering. In addition, we design specialized training strategies to enhance model stability and performance, particularly under GNSS outage conditions. The proposed method was evaluated on simulated three-dimensional data sets and real-world scenarios. The results demonstrate that the DLKF achieves improved navigation accuracy and reduced position errors during GNSS outages compared with the MBKF. More importantly, the proposed approach provides a consistent and interpretable framework for integrating DNNs into the well-established MBKF-based GNSS/ INS integration framework. These findings highlight the potential of combining data-driven methods with traditional model-based approaches to address the limitations of GNSS/INS integration and to provide enhanced solutions for autonomous systems in challenging environments.
In-space manufacturing of optical lenses: Fluidic Shaping aboard the International Space Station
Item type: Journal Article
Luria O.; Elgarisi M.; Stibbe E.; et al. (2026)
In-space manufacturing technologies are considered vital for enabling advanced space missions and addressing logistical limitations of space exploration. While additive manufacturing has progressed rapidly, it still falls short of delivering the ultra-smooth surfaces required for optical elements. Fluidic Shaping is a novel method that harnesses surface tension under microgravity to form optical components with exceptionally smooth surfaces. This study demonstrates the feasibility and potential of Fluidic Shaping as a method for manufacturing optical components in space through two experiments performed aboard the International Space Station (ISS) during the Ax-1 mission. The first experiment involved fabricating centimeter-scale polymer lenses, solidifying them via ultraviolet (UV) curing, and analyzing the resultant optics upon their return to Earth. While sub-nanometric surface smoothness was achieved, some polymer lenses displayed unexpected thermo-chemical deformations, indicating complex polymerization dynamics unique to the microgravity environment. In the second experiment, a 172 mm diameter water lens was deployed, confirming Fluidic Shaping’s scalability and demonstrating basic optical functionality through image analysis. These experiments collectively underline the technique’s relevance for both small-scale optics and large-aperture applications. Our results highlight critical considerations for future research, including optimizing polymerization processes and refining liquid-handling methods to advance practical, in-space optical manufacturing capabilities.
Item type: Person
Item type: Person
Item type: Person