states.information_state_entries
def information_state_entries(field: str, dim: int, meas_field: str | None = None, q: float = 0.01, r: float = 0.1, init_var: float = 1.0) -> dict[str, State]Build the ``(mean, infomat, infovec)`` entries for an information filter.
The mean entry holds the recovered ``(dim,)`` estimate (match it with
any embedding cost). The information-matrix and -vector entries are
no-op'd through predict/update because the mean entry's
:class:`InformationProcess` / :class:`InformationUpdate` maintain them.
Parameters
| Name | Type | Description |
|---|---|---|
| field | str | Name of the mean field (and prefix for the auxiliary fields). |
| dim | int | Embedding dimensionality. |
| meas_field = None | str | Detection field supplying the measurement. Defaults to :paramref:`field`. |
| q = 0.01 | float | Per-unit-time process-noise scale. |
| r = 0.1 | float | Measurement-noise scale. |
| init_var = 1.0 | float | Initial per-dimension variance; the spawned information matrix is ``(1 / init_var) I``. |
Returns
dict — Three :class:`~unitrack.states.State` entries keyed by ``field``, ``f"{field}_infomat"`` and ``f"{field}_infovec"``.
Source: unitrack/states/kalman/information.py:177