update docs and tutorial

Author: betaBison

README.md

@@ -70,6 +70,7 @@ In the directory organization above:
     algorithms are implemented in the `algorithms`:
 
       * Weighted Least Squares
+      * Extended Kalman Filter
       * Calculating pseudorange residuals
   * The data parsers in the `parsers` directory allow for loading
     GNSS data into `gnss_lib_py`'s unifying `NavData` class.

docs/source/index.rst

@@ -79,6 +79,7 @@ In the directory organization above:
     algorithms are implemented in the :code:`algorithms`:
 
       * Weighted Least Squares
+      * Extended Kalman Filter
       * Calculating pseudorange residuals
   * The data parsers in the :code:`parsers` directory allow for loading
     GNSS data into :code:`gnss_lib_py`'s unifying :code:`NavData` class.

notebooks/tutorials/algorithms.ipynb

@@ -47,7 +47,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "state = solve_wls(derived_data)"
+    "state_wls = solve_wls(derived_data)"
    ]
   },
   {
@@ -65,7 +65,77 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plot_map(state)"
+    "plot_map(state_wls)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c68a3316",
+   "metadata": {},
+   "source": [
+    "# Extended Kalman Filter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7bf5bfc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from gnss_lib_py.parsers.android import AndroidDerived2021\n",
+    "from gnss_lib_py.algorithms.gnss_filters import solve_gnss_ekf\n",
+    "from gnss_lib_py.utils.visualizations import plot_map"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3b26664",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load Android Google Challenge data\n",
+    "!wget https://raw.githubusercontent.com/Stanford-NavLab/gnss_lib_py/main/data/unit_test/android_2021/Pixel4XL_derived.csv --quiet -O \"Pixel4XL_derived.csv\"\n",
+    "derived_data = AndroidDerived2021(\"Pixel4XL_derived.csv\", remove_timing_outliers=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0387e03e",
+   "metadata": {},
+   "source": [
+    "Solve for the Weighted Least Squares position estimate simply by passing the measurement data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2615edfb",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "state_ekf = solve_gnss_ekf(derived_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63c71960",
+   "metadata": {},
+   "source": [
+    "Plot the ECEF x and ECEF y computed position estimate of the receiver"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "336e4a0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_map(state_ekf)"
    ]
   },
   {
@@ -103,7 +173,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "solve_residuals(galileo_data, state, inplace=True)"
+    "solve_residuals(galileo_data, state_wls, inplace=True)"
    ]
   },
   {