states.KalmanLinear
class KalmanLinear:Linear-Gaussian Kalman predict step.
Reads ``cs.{field}`` (mean, shape ``(N, D)``) and ``cs.{field}_cov``
(covariance, shape ``(N, D, D)``) and writes both back as
.. math::
x' = F x, \qquad P' = F P F^T + Q \cdot dt.
Parameters
| Name | Type | Description |
|---|---|---|
| field | str | Field name for the mean. Covariance lives in ``f"{field}_cov"``. |
| F | torch.Tensor | ``(D, D)`` state-transition matrix. |
| H | torch.Tensor | ``(M, D)`` measurement matrix. Stored here for the paired :class:`KalmanUpdate` factory; not used by the predict step itself. |
| Q | torch.Tensor | ``(D, D)`` process-noise covariance (per unit time when ``dt_scale_q=True``). |
| R | torch.Tensor | ``(M, M)`` measurement-noise covariance. Stored for the paired update; not used by the predict step itself. |
| dt_scale_q = True | bool | Multiply ``Q`` by ``ctx.delta`` before adding to the predicted covariance. Default ``True``. |
Raises
- ValueError — If ``F``, ``H``, ``Q``, and ``R`` do not all share the same dtype (would force silent per-call casting).
Members
property
- cov_fieldReturn auxiliary covariance field name.
- dt_scale_q
- field
Source: unitrack/states/kalman/base.py:14