Fighter Aircraft Without a Pilot: How Do You Build a Nervous System for an Aircraft That Must Make Its Own Decisions?

Figure 1 – The Nervous System of an Autonomous Combat Aircraft: AI can make decisions, but it depends on a continuous and reliable stream of data from the IMU, position and temperature sensors, and power monitoring systems. Together, they provide the mission computer with a real-time picture of the aircraft’s motion, mechanism positions, and overall system health.

Figure 2 – When GNSS Cannot Be Trusted, Inertial Data Keeps Flowing: A Tactical IMU provides continuous motion data to the navigation, Sensor Fusion and Edge AI systems. On a dynamic platform, latency also matters: at a rotation rate of 60°/s, a 5 ms delay already represents an angular change of 0.3°, compared with just 0.0012° at a 20 µs delay.

The Aircraft Issued a Command. How Does It Know It Was Actually Executed?

Suppose the flight control computer decides to perform a maneuver.

It calculates the required response and sends a command to an actuator to move a control surface to a specific angle.

From the software’s perspective, the command has been issued.

But from the aircraft’s perspective, that still does not mean the action was actually performed.

The actuator may move precisely to the commanded position.

It may complete only part of the required travel.

It may respond more slowly than expected.

Mechanical play may develop.

And in the event of a failure, it may not move at all.

That is why, in a critical Flight Control system, it is not enough to know what was commanded.

The system also needs to know:

What actually happened?

Command Is Not Feedback

This is one of the fundamental principles of Closed-Loop Control.

The computer sends a command:

Commanded Position: 7.3°

A position sensor measures the actual mechanical position:

Actual Position: 7.2°

The control system can now compare the two values and adjust the command accordingly.

But if the sensor reports:

Actual Position: 3.1°

when the computer expects 7.3°, the system has learned something far more important:

The system is not behaving as commanded.

In an autonomous aircraft, that difference can turn an engineering parameter into operationally significant information.

This Is Where Position Sensors Come In

Different types of sensors can be used to measure motion in different mechanical systems.

Rotary Position Sensors measure angular movement and can be used in rotating mechanisms, shafts and systems where the actual angle needs to be known.

Linear Position Sensors measure linear movement and can provide feedback on the travel of an actuator or other linear mechanism.

In an aerospace application, the selection process involves much more than deciding whether a Rotary or Linear sensor is required.

Parameters such as Resolution, Accuracy, Linearity, Repeatability, Temperature Range, Vibration, Electrical Interface, Packaging – and, in some applications, Redundancy – must also be considered.

Variohm Group, for example, offers a range of technologies for rotary and linear position measurement, allowing the sensing principle to be matched to the mechanical and environmental requirements of the application.

From the aircraft architecture perspective, however, the specific sensing technology is only part of the story.

The objective is to close the loop:

Command → Actuator → Mechanical Movement → Position Measurement → Controller

And then again:

Measure → Compare → Correct

Thousands of times throughout the mission.

What Happens When Two Sensors Disagree?

In Flight-Critical systems, simply receiving a measurement may not be enough.

The system also needs to determine whether that measurement can be trusted.

Suppose two Position Feedback channels are installed on a critical mechanism.

Sensor A reports:

7.2°

Sensor B reports:

7.3°

The system has a reasonable indication that the measurements are consistent.

But suppose the readings suddenly become:

Sensor A:

7.2°

Sensor B:

11.8°

Now the situation is entirely different.

One of the sensors may be wrong.

There may be a wiring fault.

There may be a mechanical problem.

Or the source of the fault may lie somewhere else in the system.

Redundancy does not necessarily mean simply adding “another sensor in case the first one fails.”

It can allow the system to perform Cross-Checking, detect disagreement and initiate Fault Detection logic.

This becomes particularly important in an autonomous system.

There is no pilot to feel that the aircraft is behaving abnormally and then look at the instruments to determine what is happening.

The aircraft must be able to detect for itself that the command, the mechanical movement and the dynamic response are not consistent with one another.

This Is Where the IMU and Position Sensor Begin to Speak the Same Language

And this is where things become even more interesting.

Suppose the aircraft commands a control surface to move.

The position sensor reports:

The surface has reached the commanded angle.

At the same time, the IMU measures the aircraft’s dynamic response.

If the control surface appears to be in the correct position but the aircraft does not respond as the model expects, a different kind of discrepancy has emerged.

Each sensor sees a different part of reality.

The Position Sensor sees the mechanism.

The IMU sees the dynamic response of the platform.

When the control system combines information from different sources, it can build a richer picture than either sensor could provide on its own.

This is already a step beyond simple Position Feedback toward System-Level State Awareness.

But Another Warning Sign May Appear Before the Failure

Suppose the actuator is still reaching the commanded position.

The Position Sensor detects no problem.

The aircraft’s dynamic response also appears normal.

But the motor driving the mechanism is beginning to work harder.

Its temperature is gradually increasing.

Functionally, the system is still operating.

From a health perspective, however, a failure may already be developing.

And this brings us to the next layer of the aircraft’s sensing architecture:

Temperature Sensing.

Figure 3 – Command Is Not Confirmation: In a closed-loop flight control system, sending a command to an actuator is not enough. Rotary and linear position sensors measure the actual position and provide feedback to the controller, while the IMU measures the aircraft’s dynamic response. Comparing the command, actual position, and dynamic response enables more precise control, fault detection, and redundancy.

The System Is Still Working – But Is It Beginning to Fail?

One of the interesting challenges in an autonomous system is that failure is not always a binary event.

A component does not necessarily transition instantly from “healthy” to “failed.”

Sometimes, failure begins much earlier.

A bearing begins to generate more friction.

An actuator requires more current to perform the same movement.

A motor begins to run hotter.

Power Electronics operate at a higher temperature than usual.

An electrical connector develops increased resistance.

And throughout all of this, from the mission computer’s perspective:

The system is still working.

The command is sent.

The actuator reaches the required position.

The Position Sensor confirms it.

And the aircraft continues its mission.

But something has changed.

Temperature Is More Than an Environmental Parameter

When discussing a Temperature Sensor in an aerospace system, it is easy to think primarily about measuring ambient temperature.

But on an autonomous platform, temperature measurement can also help provide insight into the condition of the system itself.

Depending on the platform architecture, sensors may monitor areas such as:

• Electric Motors
• Actuators
• Bearings
• Power Electronics
• Batteries
• Power Distribution
• Avionics
• Gearboxes
• Hydraulic Systems

The objective is not simply to know that a component is currently operating at, for example, 72°C.

A more interesting question may be:

Why did the temperature rise from 55°C to 72°C when the operating conditions barely changed?

This is where Temperature Monitoring begins to evolve from simple “Measurement” into Health Monitoring.

It Is Not Just the Temperature – It Is Also the Rate of Change

Suppose two identical actuators are operating under similar conditions.

Actuator A:

54°C → 56°C → 57°C

Actuator B:

54°C → 61°C → 69°C

Both may still be operating within their allowable temperature range.

But their behavior is clearly not the same.

An advanced Health Monitoring system can evaluate not only the absolute temperature, but also:

Rate of Change

Load

Duty Cycle

Ambient Temperature

Current Consumption

Historical Behavior

And this creates an important distinction from an autonomy perspective.

The aircraft is no longer asking only:

“Is the system working?”

It can begin asking:

“Is the system behaving the way a healthy system should behave?”

That is a significant difference.

From Temperature Sensing to Prognostics

As a system collects more data, that information can be used for Condition-Based Maintenance and potentially as part of Prognostics and Health Management – PHM.

This does not mean that a temperature sensor alone can predict a failure.

It cannot.

But it can provide another valuable indication when combined with other sources of information.

For example:

Position Sensor
Did the mechanism reach the commanded position?

Current Monitoring
How much current was required to get it there?

Temperature Sensor
How much heat was generated during operation?

IMU
Did the aircraft respond as expected?

When all of these parameters appear normal, confidence in the health of the system increases.

But suppose the mechanism still reaches the correct position while its current consumption and temperature gradually increase from one mission to the next.

The system now has information that the Position Sensor alone could never provide.

The mechanism may still be operational.

But a failure may already be developing.

On an Unmanned Aircraft, This Information Can Affect the Mission

This is where we move once again from engineering into operational decision-making.

Suppose an autonomous aircraft is at the beginning of a long-range mission.

One of its systems begins to show an abnormal Thermal Trend.

There is no Failure yet.

The system is still functioning.

But the Mission Management computer knows that the platform is hundreds of kilometers from its recovery point.

What should it do?

Continue the mission normally?

Reduce the load on the affected system?

Change the flight profile?

Transfer the function to a Redundant system?

Or determine that the risk of continuing the mission has become too high and return?

These are no longer questions about a temperature sensor.

They are questions of Autonomous Mission Management.

The sensor has simply provided the physical information on which a decision can be based.

And That Is Exactly the Role of Sensors in an Autonomous System

Variohm Group offers a range of temperature sensing technologies, including RTDs, Thermistors and application-specific Temperature Sensing solutions.

But here too, sensor selection should not begin with the question:

“Which Temperature Sensor is available in the catalog?”

It should begin with engineering questions:

What are we trying to measure?

What is the required temperature range?

What response time is required?

Where can the sensor be installed?

What is the thermal mass of the measurement point?

What Accuracy is required?

What level of resistance to Vibration, Shock and environmental conditions is required?

And most importantly:

What decision is the system expected to make from this information?

Because in an autonomous aircraft, a sensor is not valuable simply because of the number it measures.

It is valuable because of the decision that measurement can support.

Now Let’s Connect Everything: The Aircraft Needs to Know When It Is “Wounded”

So far, we have looked at different systems separately.

The IMU tells the aircraft how it is moving.

Position Sensors tell it whether mechanisms have executed their commands.

Temperature Sensors can indicate changes in the thermal condition of components and systems.

The power system can provide additional information about current, voltage and electrical faults.

But the truly interesting capability emerges when the aircraft begins to cross-check these different sources of information.

Because on the battlefield, the problem is not always a component that simply stops working.

Sometimes the aircraft is damaged.

Sometimes one system begins to degrade.

Sometimes a Sensor provides incorrect data.

And sometimes the only way to understand what has actually happened is to compare several independent sources of information.

This is where the next stage of autonomy begins:

Detect → Isolate → Reconfigure → Continue or Abort

Or, in simpler terms:

Can an autonomous combat aircraft know that it has been damaged – and determine whether it is still capable of fighting?

Figure 4 – A System Can Still Be Functional Without Being Healthy: Combining Position Feedback with current and temperature monitoring can reveal a degradation trend before it develops into a failure. This allows an autonomous system to reduce load, switch to a redundant system, modify the mission profile, or decide to return to base.

Figure 5 – Damage Does Not Necessarily Mean the End of the Mission: Combining data from the IMU, position and temperature sensors, and the electrical system can help detect anomalies, isolate the source of a fault, and assess which capabilities remain available. From there, an autonomous system can reconfigure itself and determine whether to continue the mission, modify it, or return to base.

The Autonomous Aircraft Will Not Necessarily Fly Alone

When we think about an unmanned combat aircraft, it is easy to imagine an independent platform heading out on a mission by itself.

But one of the major directions in the evolution of military aviation is actually the integration of multiple platforms.

A manned fighter may operate alongside several unmanned platforms – Collaborative Combat Aircraft (CCA) – each contributing different sensors, capabilities and mission functions.

One platform may carry a Sensor Payload.

Another may serve as a Communications Relay.

Another may carry Electronic Warfare systems.

And another may be assigned a different task as the operational situation evolves.

The objective is not necessarily to create a “robotic fighter” that completely replaces the manned combat aircraft.

Instead, the direction may be toward a distributed combat system, in which humans and autonomous platforms operate together.

But before such a team can function effectively, each platform first needs to know something fundamental:

What is my own state?

Before Sharing Targets, You Need to Share State

Suppose several unmanned aircraft are operating alongside a manned fighter.

One reports:

NAVIGATION: HIGH CONFIDENCE

FLIGHT SYSTEMS: NOMINAL

MISSION SENSOR: AVAILABLE

COMMUNICATION: DEGRADED

Another reports:

NAVIGATION: DEGRADED

FLIGHT SYSTEMS: NOMINAL

MISSION SENSOR: AVAILABLE

THERMAL STATUS: CAUTION

This information may be just as important as the location of the target.

If a Mission Management system wants to transfer a task from one platform to another, it needs to know not only which aircraft is closer, but also:

Which platform is capable of performing the mission?

Which one has the appropriate Sensor?

Which platform is in better system health?

Which one has sufficient fuel or energy remaining?

Which platform has the more reliable navigation solution?

And which aircraft may need to leave the formation and return?

In other words, collaborative Autonomy begins with the Self-State Awareness of every member of the team.

What Happens When Communications Are Not Perfect?

This is a critical issue.

A military system cannot assume that a fast, continuous and reliable data link will always exist between every platform.

The adversary may attempt to disrupt communications.

The distances involved may be significant.

Line-of-Sight conditions may change.

Bandwidth may be limited.

And some platforms may temporarily become disconnected from the network.

This is one of the reasons Edge AI becomes so important.

Instead of transmitting every image, every measurement and every decision to a centralized computer, part of the processing can take place onboard the platform itself.

The aircraft can process local information, build a State Estimate, detect anomalies and make certain decisions within the rules and authorities defined for it.

It does not necessarily need to transmit all of its Raw Data over the network.

Depending on the system architecture, it may instead share more relevant, processed information.

For example:

Position

Velocity

Heading

Navigation Confidence

Sensor Availability

System Health

Mission Capability

And once again, this brings us back to the sensors.

Edge AI Cannot Fix Bad Sensor Data

You can install an extremely powerful AI processor onboard the aircraft.

You can run advanced algorithms on it.

You can give it Computer Vision, Target Recognition and Mission Planning capabilities.

But there is a fundamental limitation:

The algorithm cannot know more than the information available to it allows it to know.

If motion data arrives late, it is working with an outdated State.

If Position Feedback is unreliable, it cannot know with confidence where the mechanism actually is.

If Thermal Monitoring is absent where it is needed, the computer cannot detect a trend that was never measured.

And if GNSS is disrupted and no additional sources of information were designed into the architecture to handle that condition, even an excellent algorithm may build an incorrect picture of reality.

That is why autonomous systems are still subject to a very simple principle:

Garbage In → Garbage Out

Or, in terms more appropriate for a combat aircraft:

Autonomy is only as good as the data feeding it.

Sensor Fusion Is Not a Sensor

The term Sensor Fusion appears in almost every discussion about autonomous systems.

But it is important to understand what it is not.

Sensor Fusion is not a magic sensor that fixes every problem.

It is a process that combines information from different sources to build a better estimate of the state of the system or its environment.

For example, a navigation system may combine information from:

GNSS

IMU

Radar / EO

Barometer

Magnetometer

Terrain or Visual References

When one source becomes less reliable, the system may adjust the weight assigned to it – provided that other sources are available and can be trusted.

The same principle can be applied to Health Monitoring.

The Position Sensor provides one piece of information.

The Temperature Sensor provides another.

The electrical system contributes additional data.

The IMU measures the resulting dynamic response.

The real value emerges when the system can cross-check the information.

This is why good Sensor Fusion begins long before the algorithm.

It begins with sensor selection.

Not Every Sensor Needs to Be “the Most Accurate”

This is an especially important point for system engineers.

It is easy to define a requirement:

Give me the most accurate sensor available.

But on an airborne platform, Accuracy is only one variable.

Other parameters may be equally important:

Latency

Bandwidth

Repeatability

Temperature Stability

Shock & Vibration

Size

Weight

Power Consumption

Electrical Interface

Redundancy

Reliability

Environmental Sealing

And, of course:

Cost

A sensor with excellent Accuracy but Latency that does not match the requirements of the control loop may be the wrong choice.

A highly accurate sensor that cannot maintain its performance across the mission temperature range may also become problematic.

And a sensor that is nearly perfect from a metrology perspective but is too large or too heavy may simply be unsuitable for an airborne platform.

Sensor selection for an autonomous platform is therefore a Systems Engineering Trade-Off.

The right question is not:

“What is the best sensor?”

It is:

“What does the system need to measure, at what quality and speed, and under what conditions, in order to make the right decision?”

SWaP-C: When Millimeters and Watts Begin to Affect the Mission

On an unmanned stealth combat aircraft, this question becomes even more significant.

Every component occupies space.

Every component adds weight.

Every component consumes power.

And every watt consumed onboard ultimately becomes heat that must also be managed.

This is why engineers use the term:

SWaP-C – Size, Weight, Power and Cost

But on a stealth platform, SWaP-C is more than an engineering convenience.

It can affect range.

Endurance.

Payload.

Thermal Management.

Reliability.

And the ability to integrate more functionality into a limited volume.

In other words, even the selection of a small sensor can ultimately become part of a much larger operational equation.

And this brings us to an interesting paradox:

The more autonomous the aircraft becomes, the more information it needs.

More information requires more Sensing, Processing and Communication.

Yet the aircraft still needs to remain compact, lightweight and efficient – and, in the case of a stealth platform, maintain a low signature.

The next-generation challenge is not simply to give the aircraft more senses.

It is to give it the right senses – without allowing the nervous system itself to become a burden.

Figure 6 – Autonomy Does Not Mean Operating Alone: In a Manned-Unmanned Teaming environment, each CCA/UCAV needs to understand its own state – position, navigation confidence, system health, and mission capability – before it can effectively share information and tasks with other platforms. Even when communications are degraded, Edge Autonomy can allow the platform to continue operating within its defined mission parameters.

Engineering Autonomy from the Sensor Up

After discussing AI, Sensor Fusion, CCA and Mission Management, it is easy to assume that the primary challenge of an autonomous combat aircraft lies in software.

But ultimately, all of this digital architecture is connected to a physical world.

An aircraft rotates.

A wing flexes under load.

A mechanism changes position.

A motor heats up.

Current increases.

Temperature changes.

A system is exposed to Vibration and Shock.

And the computer cannot “know” any of these things unless there is a way to measure them.

That is why the design of an autonomous system should begin with a fundamental question:

What does the aircraft need to know?

And only then:

How are we going to measure it?

Four Layers of Awareness

We can think of the platform’s “nervous system” in terms of four families of information.

1. Motion Awareness – What Is the Aircraft Itself Doing?

This is where the IMU and inertial navigation systems come into play.

They provide Angular Rate and Acceleration data and form part of Attitude / State Estimation, Flight Control and Navigation processes.

In an environment where GNSS may be Jammed or Spoofed, the quality of inertial information becomes even more important.

In highly dynamic applications, parameters such as:

Bias Stability

Angle Random Walk

Bandwidth

Data Rate

Message Latency

Temperature Performance

can all affect the quality of the information delivered to the systems above the IMU.

This is the type of environment for which Tactical-Grade IMUs, such as the IMU families from Gladiator Technologies, are designed.

2. Position Awareness – Where Are the Mechanisms Actually Positioned?

The computer knows what it commanded.

The position sensor tells it what actually happened in the mechanical world.

Depending on the mechanism, Rotary Position Sensors can be used to measure angular position, while Linear Position Sensors can measure linear travel.

The applications are not limited to a single type of mechanism.

Position Feedback may be relevant to Flight Control systems, opening and closing mechanisms, Steering, Servo Mechanisms, Antenna Positioning, Payload Mechanisms and other mechanical systems where the actual position needs to be known.

Variohm Group offers a range of technologies for rotary and linear position measurement, including Contacting and Contactless solutions that can be selected according to the requirements of the application.

On an airborne platform, the question is not simply:

Rotary or Linear?

It also includes:

What is the required range of motion?

What Accuracy is required?

What Repeatability is required?

What happens across the operating temperature range?

What level of Shock and Vibration resistance is required?

Is Redundancy required?

And what happens if the sensor itself fails?

3. Thermal Awareness – Where Is Stress Beginning to Develop?

Temperature can provide information about the environment, but also about the condition of the system itself.

RTDs, Thermistors and other Temperature Sensing technologies can be integrated into the monitoring of:

Electric Motors

Bearings

Power Electronics

Avionics

Batteries

Gearboxes

Mechanical Assemblies

and other systems where thermal changes may be significant.

Here too, Variohm Group offers a range of temperature sensors and application-specific sensing solutions.

But as we have seen, the real value is not always the instantaneous temperature.

Sometimes the Trend matters more.

A component operating at 70°C today is not necessarily less healthy than one operating at 55°C.

The relevant questions are what the component was designed for, what load it is operating under – and whether its behavior is changing over time.

4. Electrical Awareness – What Is Happening in the Power System?

The aircraft’s nervous system cannot ignore the electrical system that keeps it alive.

An autonomous aircraft may contain Mission Computers, communications systems, EW, Sensors, Navigation, Processing and Payloads – and every one of them depends on reliable electrical power.

A fault on one branch should not necessarily bring down the entire system.

That is why Circuit Protection, Power Distribution, Current Monitoring and Fault Isolation are also part of the platform’s survivability architecture.

From an autonomy perspective, electrical information can itself become State Data.

Not simply:

Breaker Tripped

but:

Which capability did I lose as a result?

Can the affected branch be isolated?

Is an alternative power source available?

Can the mission continue with a reduced electrical load?

This is where the power system connects back to Mission Management.

The Right Sensor Does Not Begin with a Part Number

This may be one of the most important points for an engineer at the beginning of a design.

When a customer says:

“I need a Position Sensor.”

that is not yet a complete engineering requirement.

The same applies to an IMU or a Temperature Sensor.

Selecting a component requires understanding the application.

For a Position Sensor, for example, we would want to know:

What exactly is moving?

Rotary or Linear?

What is the required Travel or Angle?

What is the speed of movement?

What Accuracy and Resolution are required?

What Output is required?

What is the supply voltage?

What is the operating temperature range?

What are the Shock and Vibration requirements?

What are the dimensional and installation constraints?

Is Redundancy required?

What operating life is required?

For an IMU, the questions are different:

What Bias performance is required?

What are the platform dynamics?

What Angular Rate Range is required?

What Bandwidth is needed?

What Data Rate?

What is the allowable Latency?

What is the operating temperature range?

What Interface is required?

What are the SWaP requirements?

And for a Temperature Sensor:

What are we measuring?

Where are we measuring it?

What temperature range?

What Accuracy?

What Response Time?

What Probe or Housing?

How will it be installed?

And what is the mechanical environment?

Only after these questions have been answered should the search for a part number begin.

Because in an Autonomous Aircraft, One Spec Almost Never Tells the Whole Story

An IMU is not “good” simply because it has low Bias.

A Position Sensor is not “good” simply because it offers high Accuracy.

A Temperature Sensor is not “good” simply because it can measure up to 200°C.

Each one operates as part of a larger system.

The real question is how its data will affect:

Control Loop

State Estimation

Fault Detection

Sensor Fusion

Health Monitoring

Mission Management

This is where component selection stops being simply Purchasing and becomes Systems Engineering.

The Next-Generation Aircraft Will Need More Than AI

The headlines about next-generation combat aircraft will most likely focus on AI.

Stealth.

Swarming.

CCA.

The ability to fly without a pilot.

And weapons.

But beneath all of these is a much less glamorous – yet equally critical – layer.

The layer that measures reality.

For an aircraft to make a decision, it first needs information.

To detect a fault, something has to measure the anomaly.

To compensate for a failure, the system first needs to know that the failure occurred.

And to continue operating when GPS, communications or another system is unavailable, the architecture needs additional sources of information that it was designed to rely on.

The entire article can therefore be summarized in one idea:

The AI decides.
The sensors make sure it has something real to decide with.

Or, put another way:

Autonomy makes the decision. The sensing system makes sure that decision is grounded in reality.

Figure 7 – The Nervous System of an Autonomous Aircraft: Motion data from the IMU, position feedback from rotary and linear sensors, thermal condition data, and electrical system protection and monitoring work together to build a reliable picture of the platform’s state. The AI makes the decision – but the sensors provide the reality on which that decision is based.

Conclusion: Autonomy Begins Long Before AI

The next generation of autonomous combat aircraft will not simply be fighter aircraft with the pilot removed.

Removing the human from the cockpit changes the requirements of the entire platform.

The aircraft needs to know how it is moving even when GNSS cannot be trusted. It needs to know whether its mechanisms are actually where they are supposed to be, detect thermal or electrical changes before they develop into failures, distinguish between a Sensor Fault and a real physical event – and assess which capabilities remain available when something goes wrong.

And as more decision-making moves to the Edge, the demand for reliable, fast and continuous information only increases.

This is why Autonomy is not just a software problem.

Beneath the AI, Sensor Fusion and Mission Computer lies a physical layer of sensors and protection systems that connects the algorithm to the real world.

A Tactical IMU provides information about the motion of the platform.

Rotary and Linear Position Sensors provide Feedback on the actual position of mechanisms.

Temperature Sensors help build a picture of the thermal condition of the platform and identify abnormal trends.

And Circuit Protection and Power Monitoring help protect critical systems, isolate faults and maintain functional continuity wherever possible.

Each measures something different.

Together, they help the aircraft answer the most important question:

What is actually happening to me right now?

Only when that question can be answered reliably can the system move on to the next one:

What should I do about it?

Designing the Sensing Architecture for Your Next Platform?

At Amironic, we work with engineers from the requirements-definition stage – from selecting Tactical IMUs from Gladiator Technologies, through Rotary and Linear Position Sensors and Temperature Sensors from Variohm Group, to Circuit Protection and electrical protection solutions for critical systems.

The objective is not to begin with a part number.

The objective is to understand what the system needs to know, at what level of performance and under what environmental conditions – and then select the appropriate sensing technology.

Because in an autonomous aircraft:

The AI decides.
The sensors provide the reality.