torchfilter.utils._sigma_points

Private module; avoid importing from directly.

Module Contents

Classes

SigmaPointStrategy	Strategy to use for computing sigma weights + selecting sigma points.
MerweSigmaPointStrategy	Sigma point selection in the style of [2].
JulierSigmaPointStrategy	Sigma point selection in this style of [1].

class torchfilter.utils._sigma_points.SigmaPointStrategy

Bases: abc.ABC

Strategy to use for computing sigma weights + selecting sigma points.

abstract compute_lambda(self, dim: int) → float

Compute sigma point scaling parameter.

Parameters:

dim (int) – Dimensionality of input vectors.

Returns:

float – Lambda scaling parameter.

abstract compute_sigma_weights(self, dim: int) → Tuple[torch.Tensor, torch.Tensor]

Helper for computing sigma weights.

Parameters:

dim (int) – Dimensionality of input vectors.

Returns:

Tuple[torch.Tensor, torch.Tensor] – Covariance and mean weights. We expect 1D float32 tensors on the CPU.

class torchfilter.utils._sigma_points.MerweSigmaPointStrategy

Bases: torchfilter.utils.SigmaPointStrategy

Sigma point selection in the style of [2].

[2] http://www.gatsby.ucl.ac.uk/~byron/nlds/merwe2003a.pdf

Keyword Arguments:

• alpha (float) – Spread parameter. Defaults to 1e-2.

• kappa (Optional[float]) – Secondary scaling parameter, which is typically set to 0.0 or 3 - dim. If None, we use 3 - dim.

• beta (float) – Extra sigma parameter. Defaults to 2 (optimal for Gaussians, as per Section 3.2 in [2]).

alpha :float = 0.01

beta :float = 2.0

kappa :Optional[float]

compute_lambda(self, dim: int) → float

Compute sigma point scaling parameter.

Parameters:

dim (int) – Dimensionality of input vectors.

Returns:

float – Lambda scaling parameter.

compute_sigma_weights(self, dim: int) → Tuple[torch.Tensor, torch.Tensor]

Helper for computing sigma weights.

Parameters:

dim (int) – Dimensionality of input vectors.

Returns:

Tuple[torch.Tensor, torch.Tensor] – Covariance and mean weights. We expect 1D float32 tensors on the CPU.

class torchfilter.utils._sigma_points.JulierSigmaPointStrategy

Bases: torchfilter.utils.SigmaPointStrategy

Sigma point selection in this style of [1].

[1] https://www.cs.unc.edu/~welch/kalman/media/pdf/Julier1997_SPIE_KF.pdf

Keyword Arguments:

lambd (Optional[float]) – Spread parameter; sometimes denoted as kappa. If None, we use 3 - dim.

lambd :Optional[float]

compute_lambda(self, dim: int) → float

Compute sigma point scaling parameter.

Parameters:

dim (int) – Dimensionality of input vectors.

Returns:

float – Lambda scaling parameter.

compute_sigma_weights(self, dim: int) → Tuple[torch.Tensor, torch.Tensor]

Helper for computing sigma weights.

Parameters:

dim (int) – Dimensionality of input vectors.

Returns:

Tuple[torch.Tensor, torch.Tensor] – Covariance and mean weights. We expect 1D float32 tensors on the CPU.