jgbrasier/KFEstimate.jl
Julia package for KF and EKF parameter estimation using Automatic Differentiation
This tool helps engineers and scientists estimate unknown parameters in dynamic systems where measurements are noisy or incomplete. You provide observed data (measurements and control actions), and it outputs the best-fit parameters for your system's model. It's designed for researchers, control engineers, or data scientists working with real-world dynamic processes.
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Use this if you need to determine the underlying physical or system parameters from time-series data using Kalman or Extended Kalman filters, especially when traditional methods like MCMC or EM are too slow or complex.
Not ideal if your system is static, you don't need state-space modeling, or you prefer purely Bayesian inference methods without gradient-based optimization.
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MIT
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Sep 10, 2021
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