.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_tracking.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_plot_tracking.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_tracking.py:


Velocity-aware clustering and tracking
======================================

Track two synthetic radar targets through crossing trajectories. Each frame
contains several noisy points per target; DBSCAN produces measurements for the
stateful Kalman/EKF tracker.

.. GENERATED FROM PYTHON SOURCE LINES 9-92



.. image-sg:: /auto_examples/images/sphx_glr_plot_tracking_001.png
   :alt: GNN tracks with radial-velocity EKF updates
   :srcset: /auto_examples/images/sphx_glr_plot_tracking_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    from __future__ import annotations

    import numpy as np
    from matplotlib import pyplot as plt

    from pyradar.base import PointCloud, TrackingConfig
    from pyradar.rsp import MultiTargetTracker, dbscan

    rng = np.random.default_rng(4)
    tracker = MultiTargetTracker(
        TrackingConfig(
            enabled=True,
            dimensions=2,
            processNoise=0.8,
            measurementNoise=0.3,
            radialVelocityNoise=0.5,
            gatingThreshold=13.8,
            confirmationHits=2,
            deletionMisses=4,
        )
    )

    measurementHistory: list[np.ndarray] = []
    trackHistory: dict[int, list[np.ndarray]] = {}
    for frameId in range(32):
        time = frameId * 0.1
        centres = np.array(
            [
                [7.0 + 0.7 * time, -2.0 + 1.1 * time, 0.0],
                [10.0 - 0.4 * time, 2.0 - 1.0 * time, 0.0],
            ]
        )
        velocities = np.array([[0.7, 1.1, 0.0], [-0.4, -1.0, 0.0]])
        xyz = np.vstack([centre + rng.normal(0, 0.09, size=(8, 3)) for centre in centres])
        fullVelocity = np.repeat(velocities, 8, axis=0)
        unit = xyz / np.linalg.norm(xyz, axis=1, keepdims=True)
        radial = np.sum(fullVelocity * unit, axis=1)
        points = PointCloud(
            xyz=xyz,
            power=np.ones(xyz.shape[0]),
            snr=np.full(xyz.shape[0], 15.0),
            radialVelocity=radial,
            sourceBins=np.zeros((xyz.shape[0], 4), dtype=np.int64),
            timestamp=time,
            frameId=frameId,
        )
        clusters = dbscan(
            points,
            eps=0.42,
            minSamples=3,
            velocityScale=0.25,
            dimensions=2,
        )
        tracks = tracker.update(points, clusters=clusters, timestamp=time)
        measurementHistory.append(clusters.centroid.copy())
        for index, trackId in enumerate(tracks.trackId):
            if tracks.status[index] != "deleted":
                trackHistory.setdefault(int(trackId), []).append(
                    tracks.position[index].copy()
                )

    figure, axis = plt.subplots(figsize=(6, 4.5), constrained_layout=True)
    measurements = np.vstack(measurementHistory)
    axis.scatter(
        measurements[:, 0],
        measurements[:, 1],
        s=10,
        alpha=0.35,
        color="0.35",
        label="cluster centroids",
    )
    for trackId, positions in sorted(trackHistory.items()):
        path = np.asarray(positions)
        axis.plot(path[:, 0], path[:, 1], marker=".", label=f"track {trackId}")
    axis.set(
        xlabel="Forward x (m)",
        ylabel="Left y (m)",
        title="GNN tracks with radial-velocity EKF updates",
    )
    axis.legend()
    axis.axis("equal")
    plt.show()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.164 seconds)


.. _sphx_glr_download_auto_examples_plot_tracking.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_tracking.ipynb <plot_tracking.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_tracking.py <plot_tracking.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_tracking.zip <plot_tracking.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_