An information-form surrogate simplifies sensor scheduling optimization for continuous-discrete Kalman filters with stochastic arrivals and supplies two-sided performance bounds.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
A modular belief-space controller using learned Belief Control Lyapunov Functions for information gathering and conformal-prediction Belief Control Barrier Functions for safety reduces reach-avoid POMDP synthesis to fast quadratic programs.
EnDKF combines ensemble Kalman filtering with directional statistics and unit quaternions to achieve lower pose tracking error than raw measurements in synthetic constant-velocity tests and FoundationPose-based head tracking.
citing papers explorer
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Scalable Sensor Scheduling for Continuous-Discrete Kalman Filtering via Information-Form Surrogate Dynamics
An information-form surrogate simplifies sensor scheduling optimization for continuous-discrete Kalman filters with stochastic arrivals and supplies two-sided performance bounds.
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Safety-critical Control Under Partial Observability: Reach-Avoid POMDP meets Belief Space Control
A modular belief-space controller using learned Belief Control Lyapunov Functions for information gathering and conformal-prediction Belief Control Barrier Functions for safety reduces reach-avoid POMDP synthesis to fast quadratic programs.
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Pose Tracking with a Foundation Pose Model and an Ensemble Directional Kalman Filter
EnDKF combines ensemble Kalman filtering with directional statistics and unit quaternions to achieve lower pose tracking error than raw measurements in synthetic constant-velocity tests and FoundationPose-based head tracking.