GPS/IMU data fusion using multisensor Kalman filtering: introduction of contextual aspects

By Francois Caron, Emmanuel Duflos, Denis Pomorski, Philippe Vanheeghe. Information Fusion, 2006

431 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/6ee86517bddded7024b6f5805fdd90bc9abae1f0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Eder Santana
2. Andrew Haden
3. Huacheng Zeng
4. Harald Schafer
5. Riccardo Biasini
6. Hossein Pirayesh

In Artificial Intelligence

1. Aharon Bar-Hillel
2. Dan Levi
3. Eder Santana
4. Huacheng Zeng
5. Ronen Lerner
6. Guy Raz
7. Hossein Pirayesh

Citations in the corpus

• Recent progress in road and lane detection: a survey (2014, background): “GPS loss-of-contact epoches can partially be tolerated using IMU integration, and the possible reliability of this combination is a current research topic [42,43].”
• A Commute in Data: The comma2k19 Dataset (2018, methodology): “Conventionally this is done by fusing global position fixes from a GNSS module with other sensors [1], [2], [3], [4].”
• Jamming Attacks and Anti-Jamming Strategies in Wireless Networks: A Comprehensive Survey (2021, methodology): “[212] Self-coherence beamforming design Noise-like constant jamming attack [213] Signal projection onto the orthogonal space of jamming signal Noise-like constant jamming attack [214] Spatial-temporal interference projection mechanism Narrowband FM radio jamming signal [215] Regret minimization fram”