02 / Field Discovery · Mission-Critical Systems
Friend / Foe
Finding the real workflow inside
a high-stakes environment.
Through 30+ interviews, simulator observation, and live-exercise shadowing, we found that friendly-force information already existed — but lived outside the commander's sightline at the moment it mattered most.
The information already existed. The problem was that it was not available where the decision happened.
Follow the workflow ↓
Situational awareness inside tanks
fails more often than we admit.
Tank commanders on M1 Abrams may have ≤5 seconds to identify a target before engaging. Friendly-force data already existed through BFT, MFoCS, and JBC-P — but commanders still had to manually cross-check grid references while smoke, dust, weather, and motion overwhelmed their optics.
Every failed identification puts lives, tanks, and missions at risk. The problem was fratricide — friendly fire caused by hesitation, second-guessing, or misidentification under extreme time pressure.
The Combat Environment
≤5 seconds to ID a target — one mistake can cost lives
Smoke, dust, weather, and motion overwhelm optics
Manual grid checks split focus when precision is critical
Hesitation means the enemy gets the upper hand
At first, this looked like a software problem.
We later learned the information was already available — in the wrong place.
The initial assumption was that a cleaner interface would improve friendly-force awareness. Operators already had access to BFT, MFoCS, and JBC-P — the gap was not missing data, but the manual cross-checks required to use it under fire.
Initial belief
Better interface → faster identification.
If operators had a better screen, they would make faster identification decisions.
What fieldwork revealed
Place the right signal inside the existing operational display.
Commanders needed faster, clearer friend-or-foe identification embedded where they were already looking — not another system to cross-check.
What the decision workflow looked like
The critical bottleneck was not information access. It was decision latency.
The Research Insight
Every additional screen increased cognitive load, instead of eliminating second-guessing.
Tank crews validated the need for identification that arrived at the point of decision — not information that required leaving the sight picture to find.
What field research surfaced
Commanders often ignored JBC-P during active engagement — there was not enough time to leave the thermal view.
Friendly-force data from BFT and MFoCS existed, but required manual grid checks that split focus under fire.
Operators relied on thermal imagery and visual confirmation; optics were routinely degraded by smoke, dust, and motion.
Crews described second-guessing identifications even when data was available — hesitation gave the enemy the advantage.
30+ interviews across JMRC and DCTC Poland validated demand for faster, clearer friend-or-foe at shooter level.
We went looking for the real workflow.
Through 30+ interviews with Staff Sergeants, Master Gunners, and Tank Commanders across JMRC Germany and DCTC Poland — plus simulator observation and live exercise shadowing — the team learned how identification decisions were actually made under time pressure.
Questions we asked
What commanders saw
What systems asked them to do
What people ignored under pressure
What changed our assumptions
Embed identification in the Commander's Display Unit —
not the operator's head.
Current Workflow
Thermal / CITV view
JBC-P cross-check
context switchBFT / MFoCS grid lookup
context switchManual grid correlation
context switchFiring decision
Proposed Workflow
Thermal / CITV view
ATR overlay — real-time friend-or-foe ID
integratedADP-01 friendly symbology
integratedFiring decision
integratedThe proposed direction was not to replace BFT, MFoCS, or JBC-P. It was to use computer vision to surface friend-or-foe identification directly inside the Commander's Display Unit — eliminating manual cross-checks at the moment of decision.
Autonomous Target Recognition
embedded in the Commander's Display Unit.
Team Tank proposed an ATR computer-vision layer for Identification Friend-or-Foe (IFF) on ground maneuver elements — starting with M1 Abrams tanks. The model would track and classify armored vehicles in real time, embedded directly in the CDU with seamless connection to BFT, MFoCS, and JBC-P.
What This Layer Makes Possible
Real-time target ID — AI classifies armored vehicles instantly
Commander-first CDU integration — no manual cross-checks
Built to fit the fight — connects with BFT, MFoCS, and JBC-P
The concept evolved through research,
not feature accumulation.
Real-time target ID
AI model tracks and classifies armored vehicles instantly from the thermal feed.
CDU integration
Embedded directly in the Commander's Display Unit — no manual cross-checks required.
ADP-01 symbology
Military-standard friendly-force symbols crews already recognize under pressure.
BFT / MFoCS / JBC-P
Seamless connection with existing battlefield systems — built to fit the fight.
Where I contributed
Research leadership
Led research strategy and conducted 30+ stakeholder interviews with SSGs, Master Gunners, and Tank Commanders across JMRC Germany and DCTC Poland — synthesizing findings into product direction.
Field discovery
Planned site visits to JMRC and DCTC Poland, coordinated stakeholder meetings, identified operational roles to interview, and captured workflow observations during simulator demonstrations.
Product strategy
Defined the ATR/IFF scope for M1 Abrams, prioritized MVP assumptions, reframed beneficiary segments around shooter-level decision dominance, and developed value propositions.
Product development
Guided MVP iterations toward CDU-embedded computer vision, helped prioritize interface decisions including ADP-01 symbology, and connected technical feasibility to product recommendations.
Commercialization (DISF-C)
Continued as Team Tank through the Defense Innovation Summer Fellowship Commercialization Program — pursuing annotated datasets, simulation testing, field integration partners, and SBIR pathways.
The most important questions were still ahead.
These are not failures. They are the work required before the concept could become a real operational product.
Technical
Access to annotated CV training datasets
Real-time model latency on CDU hardware
Integration with BFT, MFoCS, and JBC-P
Secure battlefield data access
Certification and deployment architecture
Operational
Simulation environments for overlay + model testing
Vehicle-platform variation across armor fleets
Field testing with active tank crews
Training integration and maintenance ownership
Commercial
Acquisition pathway and pilot deployment
Procurement model for ground maneuver elements
SBIR and dual-use commercialization
Partners for system integration
Research
Validation across larger troop exercises
Cognitive-load and decision-time measurement
Decision-confidence improvement metrics
Insights from acquisition teams
A good first direction makes the next questions clearer.
The Takeaway
Eliminating fratricide one troop at a time — by turning hesitation into decision dominance at shooter level.
Seconds matter.
Placement matters.
Confidence eliminates second-guessing.
What I would validate next
I would validate whether the ATR overlay reduced identification time below the ≤5-second threshold, eliminated second-guessing during simulator and rotational training, and improved decision confidence for tank commanders under realistic conditions.
Testing Loop