Fixes for GitHub issue #65.

Minor wording changes to clarify meaning.
2015-12-15 16:05:01 -08:00 · 2015-12-15 16:05:01 -08:00 · ba553f2068
commit ba553f2068
parent e12352c520
3 changed files with 85 additions and 85 deletions
--- a/06-Multivariate-Kalman-Filters.ipynb
+++ b/06-Multivariate-Kalman-Filters.ipynb
--- a/07-Kalman-Filter-Math.ipynb
+++ b/07-Kalman-Filter-Math.ipynb
--- a/08-Designing-Kalman-Filters.ipynb
+++ b/08-Designing-Kalman-Filters.ipynb
@ -3785,7 +3785,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "I have plotted the output of two different Kalman filter settings. The measurements are depicted as green circles, a Kalman filter with R=0.5 as a thin blue line, and a Kalman filter with R=10 as a thick red line. These R values are chosen merely to show the effect of measurement noise on the output, they are not intended to imply a correct design.\n",
+    "I have plotted the output of two different Kalman filter settings. The measurements are depicted as green circles, a Kalman filter with R=0.5 as a thin green line, and a Kalman filter with R=10 as a thick blue line. These R values are chosen merely to show the effect of measurement noise on the output, they are not intended to imply a correct design.\n",
    "\n",
    "We can see that neither filter does very well. At first both track the measurements well, but as time continues they both diverge. This happens because the state model for air drag is nonlinear and the Kalman filter assumes that it is linear. If you recall our discussion about nonlinearity in the g-h filter chapter we showed why a g-h filter will always lag behind the acceleration of the system. We see the same thing here - the acceleration is negative, so the Kalman filter consistently overshoots the ball position. There is no way for the filter to catch up so long as the acceleration continues, so the filter will continue to diverge.\n",
    "\n",