Fix scalar point-estimate shape for generic trackers

[FlorianPfaff]

What changed

• Normalize AbstractFilter.get_point_estimate() with atleast_1d so a one-dimensional state is represented as a length-1 state vector instead of a scalar.
• Add a regression that places a VonMisesFilter inside TrackManager and verifies the extracted point-estimate matrix has shape (1, 1).

Why

TrackManager.get_point_estimate() stacks per-track estimates along axis 1. Circular distributions can return scalar means, so a valid one-dimensional filter caused stacking to fail with an axis error. Other generic tracker code already assumes get_point_estimate().shape[0] exists, so normalizing at the base-filter interface makes the API consistent.

Validation

• Added a targeted regression reproducing the scalar-filter case.
• Diff is limited to the base point-estimate normalization and the regression test.

Created as a draft so repository CI can validate the backend matrix before merge.

[github-actions]

✅MegaLinter analysis: Success

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✅ REPOSITORY	checkov	yes		no		no	30.18s
✅ REPOSITORY	git_diff	yes		no		no	0.06s
✅ REPOSITORY	secretlint	yes		no		no	85.94s
✅ REPOSITORY	syft	yes		no		no	2.75s
✅ REPOSITORY	trivy-sbom	yes		no		no	1.92s
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✅ YAML	yamllint	11		0		0	0.79s

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