navlie.filters

Module containing many predict-correct style filters.

Functions

check_outlier(error, covariance)	Performs the Normalized-Innovation-Squared (NIS) test to identify an outlier.
generate_sigmapoints(dof, method)	Generates unit sigma points from three available methods.
mean_state(x_array, weights)	Computes a weighted mean of a list of State instances in an iterated manner, until reaching a maximun number of iterations or a small update.
run_filter(filter, x0, P0, input_data, meas_data)	Executes a predict-correct-style filter given lists of input and measurement data.
run_gsf_filter(filter, x0, input_data, meas_data)	Executes a predict-correct-style filter given lists of input and measurement data.
run_imm_filter(filter, x0, P0, input_data, ...)	Executes an InteractingMultipleModel filter

Classes

CubatureKalmanFilter(process_model[, ...])	param process_model: process model to be used in the prediction step
ExtendedKalmanFilter(process_model[, ...])	On-manifold nonlinear Kalman filter.
GaussHermiteKalmanFilter(process_model[, ...])	param process_model: process model to be used in the prediction step
GaussianSumFilter(process_model[, ...])	On-manifold Gaussian Sum Filter (GSF).
InteractingModelFilter(kf_list, ...)	On-manifold Interacting Multiple Model Filter (IMM).
IteratedKalmanFilter(process_model[, ...])	On-manifold iterated extended Kalman filter.
SigmaPointKalmanFilter(process_model[, ...])	On-manifold nonlinear Sigma Point Kalman filter.
UnscentedKalmanFilter(process_model[, ...])	param process_model: process model to be used in the prediction step

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