typo, wording

10-Unscented-Kalman-Filter.ipynb

@@ -3013,7 +3013,7 @@
     "         residual_x=residual_x, residual_z=residual_h)\n",
     "```\n",
     "\n",
-    "Next, we need to pass extra data into our `f(x, dt)` and `h(x)` functions. We want to use `move(x, dt, u, wheelbase)` for `f(x, dt)`, and `Hx(x, landmarks)` for `h(x)`. We can do this, we just have to pass the extra parameters into `predict()` and `update()` as keyword argument,s like so:\n",
+    "Next, we need to pass extra data into our `f(x, dt)` and `h(x)` functions. We want to use `move(x, dt, u, wheelbase)` for `f(x, dt)`, and `Hx(x, landmarks)` for `h(x)`. We can do this, we just have to pass the extra parameters into `predict()` and `update()` as keyword arguments like so:\n",
     "\n",
     "```python\n",
     "            ukf.predict(u=u, wheelbase=wheelbase)        \n",
@@ -3142,7 +3142,7 @@
    "source": [
     "### Steering the Robot\n",
     "\n",
-    "The steering simulation in the run above is not realistic. The velocity and steering angles never changed, which doesn't pose much of a problem for the Kalman filter. We could implement a complicated PID controlled robot simulation, but I will just generate varying steering commands using NumPy's `linspace` method. I'll also add more landmarks as the robot will be traveling much further than in the first example."
+    "The steering simulation in the run above is not realistic. The velocity and steering angles never changed, which doesn't pose much of a problem for the Kalman filter. We could implement a complicated PID controlled robot simulation, but I will just generate varying steering commands using NumPy's `linspace` method. I'll also add more landmarks as the robot will be traveling much farther than in the first example."
    ]
   },
   {