Idea for Sliding Window Coding Scheme

It occurred to me that setting up the neural network inputs as the current and 4 previous points of the covariance element could be improved. 

1. I read somewhere that the first element of the covariance matrix corresponds to the x position - could that be why the Minor Kidnapping datasets don't have a spike?

2. More important idea:  Dumbing the inputs down:
-Split past 10 timesteps into 5 windows (numbers are negotiable), and average the contents of each window into a single number
- Code each window into a 0 or a 1. 0 means "same or decreasing from previous window" and 1 means "increasing from last time".

Hopefully, a section where the robot is localized properly would look like this:

0 0 0 0 0

And a section where a temporary blip occurs would look like this:

0 1 1 0 0

And a section where things have really gone South would look like this:

1 1 1 1 1


And to simplify the outputs, make a similar coding scheme - threshold the normal error volume and call it a 0.  Anything over that is a 1.

Feed both good and bad (kidnapped) data coded as such into the network, and see what happens.