Modern missile warning and response systems are engineered around fast detection, rapid fusion, and decisive action. Their most consequential failure mode is rarely mechanical. It is informational: the system is forced to act on coordinates that appear precise but cannot be independently verified. This paper describes, at a systems level, how coordinate truth can degrade or be corrupted in ways that remain internally plausible, and why a physics-anchored approach like ALIS provides a structurally different form of resilience. The discussion is intentionally non-operational and focuses on estimation integrity, correlated error, and independent verification.
High-consequence systems are built to move faster than humans can reason. That is the point. Sensors collect measurements, fusion pipelines generate tracks, and decision processes are triggered based on location and trajectory. Under normal conditions, this works extremely well and produces the level of precision modern operators have come to expect.
Under contested or degraded conditions, the same machinery can produce the most dangerous outcome; a system can deliver clean numbers with unjustified confidence. The problem is not "we have no data." Systems are often awash in data. The problem is that the coordinate state can become less trustworthy than the interface suggests. When pressure time is high, the system's ability to prove what it knows becomes the limiting factor, not its ability to act.
Precision, integrity, and the quiet failure mode
Coordinate spoofing is often imagined as a dramatic override of a single sensor. In reality, the systems-level issue is subtler and more common: the state estimate remains internally consistent while drifting away from physical truth. The system continues to function, continues to fuse, continues to show a stable track. Yet its location claim is no longer grounded in the world.
This can arise from hostile interference, but it also arises from ordinary failure mechanisms: reference degradation, correlated sensor error, synchronization drift, model mismatch, or incorrect priors inside a fusion engine. Different root causes can converge on the same outcome: an estimator that appears coherent because its assumptions are internally consistent, even when the environment is not.
That convergence is why precision cannot be treated as a proxy for truth. Precision describes how tight an estimate appears; integrity describes whether that confidence is justified. Signal-based architecture is optimized for precision. They perform extremely well when their reference assumptions hold, stable timing, trusted coordinate frames, consistent upstream data. When those assumptions are violated, the system can keep generating narrow confidence bounds while the truth has already moved outside them.
This is the quiet failure. It doesn't announce itself as a crash. It manifests as a high-confidence output that should not have been high-confidence. In fact, interfaces often look better exactly when the situation is worse, because correlated errors reduce disagreement and suppress alarms.
The common-mode trap
Modern localization stacks share dependencies: global timing, network synchronization, satellite-derived reference frames, and upstream fused tracks. When multiple inputs depend on the same reference class, their errors become correlated. Correlated error is dangerous because it moves estimates coherently and makes disagreement less visible. Residual checks weaken. Confidence calibration becomes optimistic. The system loses the ability to detect that its coordinate foundation is compromised.
This is where "more sensors" becomes the wrong instinct. Adding sensors that share the same assumptions increases data volume but does not increase independent truth. It can even make the system more confident for the wrong reasons. In estimation terms, observability degrades in the regimes that matter most: the system can no longer uniquely constrain the state from independent evidence within the decision horizon.
Interceptors are designed to act on a state estimate. They are not designed to prove that the state estimate remains truthful under contested reference conditions. Missile defense can push back trajectories; it cannot, by itself, validate that the coordinate frame feeding the decision is intact. The hardest questions sit above the interceptor layer: how much confidence should we assign to the track, to the origin inference, to the attribution logic, to the implied intent? When coordinate integrity is uncertain, the risk is not only operational failure. The risk is decision failure, acting decisively on a coordinate claim that cannot be justified in time.
An illustrative case
Consider a maritime asset reporting its position via satellite relay. The coordinates arrive at the operations center looking clean: consistent timestamp, plausible velocity, stable track. Downstream systems accept the update and adjust their situational picture accordingly.
What the fusion pipeline cannot know is that the asset's reference frame has been compromised. The reported coordinates are internally coherent. Velocity matches heading. Track is smooth. Timestamps are synchronized. But the position itself has drifted from physical truth. The error is not detectable by examining the coordinate stream alone, because the stream was never designed to prove its own validity.
A physics-anchored verification layer asks a different question: Is this claimed position consistent with what the environment would produce at that location? If the asset were at the reported coordinates, certain physical observables would fall within predictable bounds. If those observables are inconsistent with the claim, falling outside the envelope that physics permits, the discrepancy surfaces as a flag, not as a silent pass-through. The flag does not assert where the asset is. It asserts that the claimed position cannot be justified by independent evidence. In high-consequence contexts, that distinction matters. "Coordinates accepted" versus "coordinates verified" can be the difference between a confident decision and a justified one.
ALIS as a physics-anchored alternative
ALIS exists because coordinate truth should not be an act of faith. The central architectural choice in ALIS is simple: location is not treated as an externally asserted variable. It is treated as an inferred physical state constrained by locally observable evidence. ALIS derives location from environmental structure, including spatial signatures and motion consistency, and checks that evidence against deterministic environmental memory. The point is not that any single observable is sufficient. The point is that the environment provides an independent physical reference that does not share the same failure modes as satellite or network authority.
Where a signal-centric stack asks, "What coordinates were reported?", ALIS asks, "What location is consistent with the physics we are measuring right now?" That shift matters because it introduces a falsification path. If a claimed coordinate is inconsistent with local environmental evidence, ALIS can surface that inconsistency rather than silently propagating it. ALIS does not promise perfect certainty. It changes the shape of failure. Instead of collapsing into hidden uncertainty, where the system looks confident but is wrong, ALIS is designed to keep uncertainty honest and bounded by independent physical evidence. In practical terms, it provides a way to stress-test location claims against the environment itself, which is the only reference that remains when external authority is contested.
The most dangerous failure in coordinate-dependent systems is not signal loss. It is the continuation of operations with confidence that is no longer justified. In missile warning and defense contexts, this becomes existential: decisions are time-bound, consequences are irreversible, and false certainty can be worse than uncertainty. Signal-based systems will remain essential. But without independent, physics-anchored verification, they inherit common-mode vulnerabilities that cannot be solved by adding more of the same. ALIS exists to close that gap. Not by replacing existing systems, but by restoring something modern stacks often assume instead of measure: coordinate truth.