From dcbc9c4e964e6b0459a587a0bbbaf8d9dda072bb Mon Sep 17 00:00:00 2001 From: SonienTaegi Date: Tue, 20 Sep 2016 11:07:59 +0900 Subject: [PATCH 1/2] mkf_internal.py : Fix duplicated parameter plt.scatter(...) plt.scatter(...) can accept either 'c' or 'color' parameter but not both. This makes assert exception. --- code/mkf_internal.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/code/mkf_internal.py b/code/mkf_internal.py index 49c82d7..9f0c02c 100644 --- a/code/mkf_internal.py +++ b/code/mkf_internal.py @@ -148,7 +148,7 @@ def show_position_prediction_chart(): plt.xticks(np.arange(1,5,1)) plt.yticks(np.arange(1,5,1)) - plt.scatter ([4], [4], c='g',s=128, color='#8EBA42') + plt.scatter ([4], [4], s=128, color='#8EBA42') ax = plt.axes() ax.annotate('', xy=(4,4), xytext=(3,3), arrowprops=dict(arrowstyle='->', From cb05709cf4cf29ebc1ea8ad3b018224097afb532 Mon Sep 17 00:00:00 2001 From: Austin Date: Fri, 23 Sep 2016 14:46:28 -0500 Subject: [PATCH 2/2] Update 07-Kalman-Filter-Math.ipynb Line 757: Corrected typo in the following line; ... sphere moving through the air, but that leaves many unknown factors ... --- 07-Kalman-Filter-Math.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/07-Kalman-Filter-Math.ipynb b/07-Kalman-Filter-Math.ipynb index 613df43..bb015d1 100644 --- a/07-Kalman-Filter-Math.ipynb +++ b/07-Kalman-Filter-Math.ipynb @@ -754,7 +754,7 @@ "source": [ "## Design of the Process Noise Matrix\n", "\n", - "In general the design of the $\\mathbf Q$ matrix is among the most difficult aspects of Kalman filter design. This is due to several factors. First, the math requires a good foundation in signal theory. Second, we are trying to model the noise in something for which we have little information. Consider trying to model the process noise for a thrown baseball. We can model it as a sphere moving through the air, but that leave many unknown factors - the wind, ball rotation and spin decay, the coefficient of drag of a ball with stitches, the effects of wind and air density, and so on. We develop the equations for an exact mathematical solution for a given process model, but since the process model is incomplete the result for $\\mathbf Q$ will also be incomplete. This has a lot of ramifications for the behavior of the Kalman filter. If $\\mathbf Q$ is too small then the filter will be overconfident in its prediction model and will diverge from the actual solution. If $\\mathbf Q$ is too large than the filter will be unduly influenced by the noise in the measurements and perform sub-optimally. In practice we spend a lot of time running simulations and evaluating collected data to try to select an appropriate value for $\\mathbf Q$. But let's start by looking at the math.\n", + "In general the design of the $\\mathbf Q$ matrix is among the most difficult aspects of Kalman filter design. This is due to several factors. First, the math requires a good foundation in signal theory. Second, we are trying to model the noise in something for which we have little information. Consider trying to model the process noise for a thrown baseball. We can model it as a sphere moving through the air, but that leaves many unknown factors - the wind, ball rotation and spin decay, the coefficient of drag of a ball with stitches, the effects of wind and air density, and so on. We develop the equations for an exact mathematical solution for a given process model, but since the process model is incomplete the result for $\\mathbf Q$ will also be incomplete. This has a lot of ramifications for the behavior of the Kalman filter. If $\\mathbf Q$ is too small then the filter will be overconfident in its prediction model and will diverge from the actual solution. If $\\mathbf Q$ is too large than the filter will be unduly influenced by the noise in the measurements and perform sub-optimally. In practice we spend a lot of time running simulations and evaluating collected data to try to select an appropriate value for $\\mathbf Q$. But let's start by looking at the math.\n", "\n", "\n", "Let's assume a kinematic system - some system that can be modeled using Newton's equations of motion. We can make a few different assumptions about this process. \n",