Inside / Outside

External Motion shows where the aircraft is. Internal System Activity explains how the signal behaves.

Satellite data does not directly present a flight path. Instead, it captures a layered signal—one that reflects both the physical movement of the aircraft and the internal behavior of the systems transmitting that signal. These two influences exist simultaneously, and without separating them, the data can appear inconsistent or open to interpretation.

This work begins by isolating those domains. External motion is governed by position, direction, and continuity over time. Internal system activity reflects the behavior of onboard electronics, including timing stability and signal response. When combined, they form the signal record. When separated, they become understandable.

Understanding the Signal as a Constraint System

The foundation of this approach is simple: the signal is not treated as a story—it is treated as a set of constraints. Burst Timing Offset (BTO) establishes distance. It defines where the aircraft must be relative to the satellite at a given moment. Burst Frequency Offset (BFO) reflects motion. It provides insight into how the aircraft is moving through space based on Doppler behavior. On their own, these measurements are limited. Together, they begin to define a structure. This framework does not attempt to interpret the signal in isolation. Instead, it applies a sequence of constraints:

• Distance must remain consistent
• Direction must match Doppler behavior
• Motion must be continuous over time
• Directional changes must align with signal shifts

Each constraint reduces uncertainty. Each removes invalid possibilities. What remains is not assumed—it is derived.

Separating Motion from System Behavior

One of the key challenges in interpreting satellite data is that motion and system behavior are intertwined. Changes in the signal can result from actual aircraft movement, or from internal system variations. Without separating these effects, it becomes difficult to determine whether a shift in the data reflects a real change in trajectory or a response from the system itself.

This framework addresses that by establishing a boundary between the two:

• External motion is evaluated through geometry and continuity
• Internal system activity is evaluated through signal behavior patterns

By treating these as distinct but interacting domains, the signal becomes more stable and more predictable. This allows motion to be evaluated independently of system noise, while still accounting for its influence.

The result is a cleaner interpretation—one that can be tested and repeated.

From Possibility to Elimination

Rather than attempting to prove a single path, this approach evaluates multiple candidate trajectories under the same conditions.

Each candidate is subjected to the same sequence of tests. If a path fails to meet any constraint—distance, direction, continuity, or alignment—it is removed. This process continues until only paths that satisfy all conditions remain.

The goal is not to force a conclusion. It is to eliminate what cannot happen. This distinction is important. A model built on assumption can support multiple outcomes. A model built on constraint removes them.

A Framework Designed to Be Tested

This work is not presented as a final answer. It is presented as a method.

A method that can be:

• applied
• challenged
• reproduced

The structure has been built so that independent reviewers can run their own simulations, test alternative paths, and evaluate results using the same criteria. 

If the framework is incorrect, it should fail under testing.

If it is consistent, it should continue producing the same constrained outcome.

Why This Approach Matters

The challenge in the search for MH370 has not been the absence of data. It has been the difficulty of turning that data into a usable constraint. Without constraint, multiple interpretations remain viable. With constraint, the number of viable paths decreases.

This framework is designed to move from uncertainty toward resolution by applying structure to the signal. Not by adding complexity, but by removing what does not fit.

After more than a decade, the objective remains unchanged:

• To build a system that is clear enough to test,
• strong enough to challenge,
• and structured enough to produce a meaningful result.

What you’re seeing here isn’t a theory. It’s a process. We started with raw satellite signal data—timing, frequency shifts, and system behavior. Instead of trying to guess what happened… we broke the signal apart. We isolated what comes from the aircraft…
and what comes from the system itself. Once those signals are separated, patterns begin to appear. Not assumptions— measurable patterns. Changes in motion… system resets… repeating hourly check-ins… Each one produces a signature. And each of those signatures can be tested. We don’t need to recreate the entire method to validate the results. If a signal represents real behavior, it will show up the same way—in simulation, in modeling, and under constraint. 

What we’ve done is take complex data and reduce it into something testable. Step by step. Signal by signal. Until what remains…
is a coherent picture. Not everything at once— but layer by layer. This is how we move from data… to clarity.