Removed warning that chapter cannot be read.

Code is working well now.

13_Adaptive_Filtering.ipynb

@@ -274,8 +274,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**author's note: this chapter is in heavy development - read it if you want, but there are bugs in the sw, a lot of stuff if being revised, text may not match the plots, etc**\n",
-    "\n",
     "So far we have considered the problem of tracking objects that are well behaved in relation to our process model. For example, we can use a constant velocity model track an object moving in a straight line. So long as the object moves in a straight line at a reasonably constant speed, or varies it's track and/or velocity very slowly this filter will perform very well. Suppose instead that we are trying to track a maneuvering target, by which I mean an object with control inputs, such as a car along a road, an aircraft in flight, and so on. In these situations the filters perform quite poorly. Alternatively, consider a situation such as tracking a sailboat in the ocean. Even if we model the control inputs we have no way to model the wind or the ocean currents. \n",
     "\n",
     "A first order approach to this problem is just to make the process noise $\\mathbf{Q}$ larger to account for the unpredictability of the system dynamics. While this can *work* in the sense of providing a non-diverging filter, the result is typically far from optimal. The larger $\\mathbf{Q}$ results in the filter giving more emphasis to the noise in the measurements. We will see an example of this shortly.\n",