Stalled.

Attempt

I have been trying to add heuristic layers to matrix matching program for a microcontroller (and programmable gate arrays), that tries to search a small two dimensional matrix inside a two dimensional matrix hyperplane (localized representation of globe). The elements of the matrices are elevation data (hills and valleys) up to a certain resolution. The hyperplane matrix is pre-loaded inside the microcontroller and the smaller matrix is provided as serial input. Ideally the production rig (which can be embedded inside an aerial vehicle), would consist of upper band radar / lidar for input. For simulation purposes, a large matrix can be generated (that follows a degree of gradient smoothness and other similar rules), and a candidate can be picked from the large matrix to act as the smaller matrix (with added noise). I tried this on a cheap microcontroller but the directional vectors that were being output were far from the expected vectors - might as well be labelled random.

TODO

Need more reading and experimentation.

Z Coordinate

It can be difficult to match the Z-Coordinate. Here’s an example:

My poems are result of my reveries. Original but inspired by spelling bees (note that it is Mis·un·der·stan·ding and not Miss·Under·Standing), Water Under the Bridge (Adele) and Stable Matching Problem.

Alright, I am off to play with coordinate compression.

References

Some definitions one might need to know to understand the article:

• TERCOM
• Manifold
• Distance
• Topography
• LIDAR
• INS
• CPLD
• Polynomial Complexity
• Heuristic

Academic References

TODO