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DOP notebook completed
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‎docs/source/tutorials/tutorials.rst‎

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utils/tutorials_constants_notebook
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utils/tutorials_coordinates_notebook
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utils/tutorials_dop_notebook
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utils/tutorials_ephemeris_downloader_notebook
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utils/tutorials_file_operations_notebook
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utils/tutorials_filters_notebook

‎docs/source/tutorials/utils/tutorials_dop_notebook.nblink‎

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{
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"path": "../../../../notebooks/tutorials/utils/dop.ipynb"
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}

‎gnss_lib_py/utils/dop.py‎

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"""Metrics to quantify quality of state estimates or GNSS measurements.
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"""
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Dillution of Precision (DOP) calculations and interface with NavData.
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"""
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__authors__ = "Ashwin Kanhere, Daniel Neamati"

‎notebooks/tutorials/utils/dop.ipynb‎

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"Dillution of Precision (DOP) describes the effect of satellite geometry on the \n",
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"user's positioning accuracy. \n",
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"DOP can refer to multiple ideas:\n",
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"1. The full DOP matrix (or tensor),\n",
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"2. DOP in the horizontal or vertical direction,\n",
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"3. DOP on the time uncertainty, or\n",
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"1. DOP in the horizontal or vertical direction,\n",
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"2. DOP on the time uncertainty,\n",
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"3. The full DOP matrix, or\n",
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"4. DOP in a particular direction.\n",
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"\n",
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"Here we show how to make use of existing functionality."
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"Here we show how to make use of existing functionality. \n",
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"For more details on the underlying math, please see the [Navipedia page on \n",
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"positioning error](https://gssc.esa.int/navipedia/index.php?title=Positioning_Error)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import gnss_lib_py as glp\n",
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"import numpy as np\n",
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"\n",
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"# A library for url downloads that works regardless of `wget` command\n",
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"import urllib.request"
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]
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},
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"glp.make_dir(\"../data\")\n",
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"# %wget https://raw.githubusercontent.com/Stanford-NavLab/gnss_lib_py/main/data/unit_test/google_decimeter_2022/device_gnss.csv --quiet -nc -O \"../data/device_gnss.csv\"\n",
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"\n",
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"# %wget https://raw.githubusercontent.com/Stanford-NavLab/gnss_lib_py/main/data/unit_test/google_decimeter_2022/device_gnss.csv --quiet -nc -O \"../data/device_gnss.csv\"\n",
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"urllib.request.urlretrieve(\"https://raw.githubusercontent.com/Stanford-NavLab/gnss_lib_py/main/data/unit_test/google_decimeter_2022/device_gnss.csv\", \"../data/device_gnss.csv\")\n",
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"\n",
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"navdata = glp.AndroidDerived2022(\"../data/device_gnss.csv\")"
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"metadata": {},
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"source": [
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"Since we already have elevation and azimuth data available to us, we can simply\n",
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"call the `get_dop` function."
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"call the `get_dop` function, which will only return the HDOP and VDOP values,\n",
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"by default.\n",
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"HDOP corresponds to the dilution of precision in the horizontal direction\n",
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"(i.e., East and North plane), and VDOP corresponds to the dilution of precision\n",
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"in the vertical direction (i.e., the Up axis)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" gps_millis HDOP VDOP\n",
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"0 1.303771e+12 0.558578 0.830517\n",
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"1 1.303771e+12 0.549160 0.829362\n",
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"2 1.303771e+12 0.558593 0.830452\n",
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"3 1.303771e+12 0.549176 0.829296\n",
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"4 1.303771e+12 0.549185 0.829263\n",
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"5 1.303771e+12 0.549193 0.829230\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"dop_navdata = glp.get_dop(navdata)\n",
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"print(dop_navdata)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Some applications may care about the dilution of precision in time (TDOP), but\n",
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"may not be interested in the dilution of precision in position. \n",
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"Simply pass this information to the parser."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"dop_navdata = glp.get_dop(navdata, HDOP=False, VDOP=False, TDOP=True)\n",
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"print(dop_navdata)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Below we illustrate all supported DOP types. The full DOP matrix (in ENU) is \n",
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"\n",
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"$$Q = \n",
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"\\begin{bmatrix}\n",
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" q_{ee} & q_{en} & q_{eu} & q_{et} \\\\\n",
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" q_{ne} & q_{nn} & q_{nu} & q_{nt} \\\\\n",
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" q_{ue} & q_{un} & q_{uu} & q_{ut} \\\\\n",
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" q_{te} & q_{tn} & q_{tu} & q_{tt} \\\\\n",
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"\\end{bmatrix}$$\n",
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"\n",
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"The matrix is symmetric (i.e., $q_{en} = q_{ne}$). \n",
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"Often the elements along the diagonal are of primary interest\n",
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"$$Q = \n",
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"\\begin{bmatrix} \n",
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" \\text{EDOP}^2 & \\cdot & \\cdot & \\cdot \\\\\n",
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" \\cdot & \\text{NDOP}^2 & \\cdot & \\cdot \\\\\n",
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" \\cdot & \\cdot & \\text{VDOP}^2 & \\cdot \\\\\n",
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" \\cdot & \\cdot & \\cdot & \\text{TDOP}^2\n",
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"\\end{bmatrix}$$\n",
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"\n",
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"\n",
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"To store the dop matrix $Q$ in `dop_navdata`, the upper triangle is\n",
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"splatted across columns to enable fast storage and access in the navdata using\n",
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"numpy."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"dop_navdata = glp.get_dop(navdata, GDOP= True, HDOP=True, VDOP=True, \n",
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" PDOP=True, TDOP=True, dop_matrix=True)\n",
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"print(dop_navdata)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We can recover the unsplatted versions of the DOP matrix as needed if we \n",
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"loop through time."
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]
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},
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"for timestamp, _, dop_navdata_subset in glp.loop_time(dop_navdata, 'gps_millis'):\n",
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"\n",
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" labels = glp.get_enu_dop_labels()\n",
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"\n",
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" dop_matrix_splat = np.array(\n",
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" [dop_navdata_subset[f'dop_{label}'] for label in labels])\n",
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"\n",
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" print(f\"At time {timestamp} the DOP matrix is\")\n",
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" print(glp.unsplat_dop_matrix(dop_matrix_splat))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Lastly, we can compute the contribution in a particular direction with numpy."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"direction_of_interest = np.array([-1, 1, 0, 0])\n",
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"# Normalize the direction of interest\n",
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"direction_of_interest = direction_of_interest / np.linalg.norm(direction_of_interest)\n",
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"\n",
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"for timestamp, _, dop_navdata_subset in glp.loop_time(dop_navdata, 'gps_millis'):\n",
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"\n",
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" labels = glp.get_enu_dop_labels()\n",
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"\n",
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" dop_matrix_splat = np.array(\n",
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" [dop_navdata_subset[f'dop_{label}'] for label in labels])\n",
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" dop_matrix_unsplat = glp.unsplat_dop_matrix(dop_matrix_splat)\n",
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"\n",
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" dop_in_direction = np.sqrt(\n",
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" direction_of_interest @ dop_matrix_unsplat @ direction_of_interest)\n",
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"\n",
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" print(f\"At time {timestamp} the DOP in the direction of interest is {dop_in_direction}\")"
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]
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}
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],
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"metadata": {

‎tests/utils/test_dop.py‎

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"""
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Test for metrics, such as DOP calculations.
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Test for DOP calculations and manipulations.
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"""
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