rlabbe/filterpy

Features

• Recursive Bayesian Updates - Implements the recursive prediction and update cycle fundamental to Bayesian state estimation.
• Kalman Filter Implementations - Provides a comprehensive library of linear and non-linear Kalman filter implementations for dynamic system state estimation.
• State Estimation Libraries - Computes probability distributions of system states using discrete Bayes and generalized filters.
• Probabilistic Estimation Toolkits - Provides a toolkit for calculating probability distributions and hidden states using discrete Bayes and generalized filters.
• Dynamic System Modeling - Merges noisy measurements with mathematical evolution models to track the state of dynamic systems.
• Hidden State Estimation - Implements Bayesian methods to estimate the internal states of dynamic systems using behavioral models and noisy data.
• Non-Gaussian State Estimation - Implements routines using Markov Chain Monte Carlo and resampling to estimate states in non-Gaussian environments.
• Non-Linear State Estimation - Tracks systems with non-linear dynamics using particle filters and Markov Chain Monte Carlo methods.
• Particle Filter Sampling - Implements particle filtering using discrete samples and resampling to handle non-Gaussian state distributions.
• State Covariance Modeling - Uses linear algebra and covariance matrices to track the mean and uncertainty of a system state over time.
• Particle Filtering - Estimates states in non-Gaussian environments using Markov Chain Monte Carlo routines and resampling.
• Discrete Bayes Filtering - Calculates probability distributions over a discrete set of states to identify the most likely current state.
• Taylor Series Approximations - Approximates non-linear system dynamics using Taylor series expansions to maintain Gaussian state distributions.
• Sensor Noise Filtering - Implements generalized structures for reducing noise and providing critical damping in raw sensor signals.
• Fixed-Lag Smoothing Algorithms - Implements fixed-lag smoothing to refine historical state estimates using a window of future data.
• Least Squares Estimators - The project derives the most probable state of a system using least squares and fading memory filters.
• Dynamic System Simulators - Generates noise matrices and simulates uncertainty within physical systems for algorithm validation.
• Statistical Analysis Libraries - Provides a framework for Gaussian analysis using Mahalanobis distance, log-likelihood, and covariance ellipses.
• Likelihood Evaluation - Evaluates the quality of state estimates by calculating the log-likelihood of observed measurements.
• Time Series Signal Smoothing - Reduces noise in sensor data using backward smoothing and fading memory filters to refine state estimates.
• Time Series Smoothing - Refines previous state estimates through fixed-lag and backward smoothing techniques.
• Time Series Smoothing Libraries - Ships a suite of algorithms for noise reduction via fixed-lag and backward smoothing.

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