# Demo Trade Study Brief

## Scenario

Persistent monitoring trade study with a border corridor, a likely ingress lane, and a logistics hub that carries the highest mission weight.

## Best-Scoring Configurations

| strategy | drones | radius | mission_fit | weighted_cov | priority_cov | priority_persist | redundancy |
| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
| patrol | 3 | 5 | 0.816 | 0.884 | 0.875 | 0.863 | 0.909 |
| patrol | 6 | 7 | 0.802 | 0.993 | 0.993 | 0.893 | 0.971 |
| patrol | 3 | 7 | 0.797 | 0.841 | 0.812 | 0.904 | 0.950 |
| patrol | 9 | 7 | 0.781 | 0.995 | 0.996 | 0.916 | 0.981 |
| patrol | 6 | 5 | 0.781 | 0.976 | 0.995 | 0.846 | 0.950 |

## Strategy Takeaways

- Best static config: `n=9`, `r=7` with mission-fit score `0.480`.
- Best patrol config: `n=3`, `r=5` with mission-fit score `0.816`.
- Static tends to hold stronger persistence over priority cells, while patrol trades some revisit discipline for broader weighted coverage.
- Redundancy is now explicit in the outputs, which makes it easier to explain when adding platforms increases overlap more than mission value.

## How To Use This Demo

1. Review the weighted-coverage heatmaps for static vs patrol.
2. Compare global coverage against priority-cell coverage.
3. Use the timeseries plot to explain when patrol starts outperforming static on mission-weighted reach.
4. Treat `mission_fit_score` as a demo-only ranking aid, not an objective truth.