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ENTITY extended Kalman filter

extended Kalman filter

PulseAugur coverage of extended Kalman filter — every cluster mentioning extended Kalman filter across labs, papers, and developer communities, ranked by signal.

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RECENT · PAGE 1/1 · 6 TOTAL
  1. TOOL · CL_84910 ·

    UAVs fuse depth camera and AI for safer person tracking

    Researchers have developed a new system for Unmanned Aerial Vehicles (UAVs) that fuses depth camera data with deep learning techniques to accurately estimate and maintain a safe distance from individuals. This approach …

  2. RESEARCH · CL_38214 ·

    Score Kalman Filter bypasses partition function for nonlinear Bayesian filtering

    Researchers have developed the Score Kalman Filter (SKF), a novel approach to nonlinear Bayesian filtering that bypasses the computationally expensive partition function. By integrating score matching with Stein's ident…

  3. RESEARCH · CL_36355 ·

    New Kalman Filter framework models complex time-series data on cell complexes

    Researchers have developed a new topology-aware state space framework for inferring latent dynamics from complex time-series data. This approach utilizes stochastic partial differential equations on cell complexes to mo…

  4. TOOL · CL_15715 ·

    Autonomous vehicle fuses sonar and GPS for precise seabed mapping

    Researchers have developed a new framework for seabed mapping in challenging shallow, turbid waters using autonomous surface vehicles. This system fuses sonar data with GPS and IMU readings, employing Fourier-Mellin tra…

  5. RESEARCH · CL_11519 ·

    Bayesian Neural Kalman Filter enhances UAV state estimation in noisy environments

    Researchers have developed a new Bayesian Neural Kalman Filter (BNKF) to improve state estimation for unmanned aerial vehicles (UAVs) in challenging environments. This hybrid framework combines Bayesian Neural Networks …

  6. RESEARCH · CL_06880 ·

    Physics-informed neural networks estimate liquid-liquid separation phase heights

    Researchers have developed a novel framework utilizing Physics-Informed Neural Networks (PINNs) to estimate the dense-packed zone height in liquid-liquid separation processes. This approach combines a PINN, pre-trained …