What Radar Engineering Taught Me About Using AI in New Domains

I’ve been using the Biohub Cell Tracking During Development Kaggle competition as a research environment to explore an unusual question:

How much of the thinking used in radar and classical signal processing can transfer to 3d biological cell tracking?

My starting point was not, which deep learning model should solve this?

It was a set of more familiar engineering questions

What is signal?
What is clutter?
How should thresholds adapt locally?
How do you suppress duplicate detections?
How do you associate objects through time when observations become ambiguous?

That led me to experiment with ideas such as CFAR-style adaptive thresholding, spatial suppression, watershed-based localization, classical geometry, and graph-based association.

Some concepts transferred surprisingly well. Others failed in useful ways.

A good example was CFAR (Constant False Alarm Rate).

Adaptive thresholding fit fluorescence microscopy naturally because background intensity changes across the volume. But the literal mathematics of classical CA-CFAR relied on statistical assumptions that did not match normalized voxel intensities.

The principle transferred. The assumptions did not.

That mismatch eventually produced detection collapse under certain conditions.

Another experiment explored whether morphology could provide an early signal of cell division. A small initial sample looked promising. A larger controlled comparison showed that the signal had essentially no discriminative value.

The hypothesis was rejected rather than tuned until it appeared successful.

Those experiments reinforced a lesson I’ve come to value:

Cross-domain engineering works best when we transfer principles, not assumptions.

And I think the same lesson applies far beyond signal processing.

As AI systems move into new operational domains, especially government, infrastructure, healthcare, and other high-consequence environments, it is tempting to transfer methods because they worked somewhere else.

But a method can behave correctly under one set of assumptions and fail completely under another.

The important questions become:

What assumptions does this system depend on?
Do they still hold in the new environment?
How would we know when they stop holding?
What evidence would justify deployment or the next iteration?

That is where evaluation, observability, provenance, failure boundaries, and human oversight become engineering requirements rather than abstract governance concepts.

A model or technique does not become trustworthy simply because it performed well in another domain.

Its assumptions have to be tested again.

For me, the most valuable part of this kind of work is not simply whether a method improves a metric.

It is understanding why something works, why it fails, and what evidence is strong enough to justify the next experiment.

The lesson turned out to be relevant beyond cell tracking: methods that work in one technical environment can fail when their underlying assumptions are silently carried into another

Principles can transfer across domains. Assumptions have to earn their way back in.