What does the system reveal while it is sensing?
Many of the most impressive advances in modern computer vision rely on active sensing: laser illumination, structured light, time-of-flight measurements or other signals transmitted into the environment.
These techniques can provide extraordinary information.
They also remind us that sensing is rarely free.
It consumes power and compute. It may require external infrastructure. And when a system actively emits energy, that emission can become part of its operational signature.
For tactical systems, this changes the design equation.
Active sensing is becoming extraordinarily capable
Recent advances in computational imaging are pushing active sensing into conditions that would previously have been extremely difficult.
Single-photon imaging can extract depth under intense ambient illumination. Non-line-of-sight imaging can reconstruct information about objects outside the direct field of view. Structured illumination can support depth estimation and navigation in darkness.
These developments matter because they extend machine perception beyond the limitations of conventional imaging.
But the underlying architecture is important.
A system that works in daylight despite overwhelming background photons is not necessarily passive. A time-of-flight system may still transmit laser pulses and measure their return. Similarly, a system capable of operating in darkness may achieve that performance through active illumination.
This distinction is easy to overlook when capability is described only in terms of what the sensor can see.
Operationally, however, active and passive sensing solve different problems and create different trade-offs.
What is the difference between active and passive sensing?
Active sensors transmit energy into the environment and analyze the response.
LiDAR, radar and structured-light systems are common examples.
Passive sensors rely primarily on energy already present in the environment. Visible-light cameras, thermal imagers and many forms of passive electro-optical sensing fall into this category.
Neither approach is inherently better.
Active sensing can deliver highly accurate range and depth information under conditions where passive systems struggle. Passive sensing can reduce dependence on transmitted energy and may lower the platform’s observable signature.
The correct architecture depends on the mission.
And in contested environments, that mission context is becoming increasingly important.
Every sensor has an information-signature trade-off
A useful way to think about tactical sensing is as a balance between two variables:
information gained and signature created.
The objective is no longer necessarily to maximize the amount of information captured.
It is to capture enough reliable information to complete the mission while minimizing unnecessary dependencies and exposure.
That changes how sensing architectures should be evaluated.
A sensor may offer greater absolute performance but require more power, greater processing capacity or active emissions.
Another system may provide less raw information but be able to operate passively, locally and independently of external signals.
For a laboratory benchmark, the first system may appear superior.
For a platform operating in a disrupted or contested environment, the second architecture may provide greater operational value.
Passive sensing is advancing too
Some of the most interesting computer-vision research is therefore not simply trying to increase sensor performance. It is finding ways to extract more information from passive measurements.
Event cameras can capture changes in a scene with extremely high temporal efficiency. Thermal imaging can reveal information invisible to conventional cameras. Depth can be estimated from optical effects such as defocus. Low-light algorithms are extracting usable information from increasingly limited photon budgets.
The underlying direction is important:
Better algorithms can sometimes replace additional illumination, additional sensing hardware or additional data.
This does not eliminate the need for active sensors. But it broadens the range of missions in which passive architectures become viable.
The edge is learning where not to look
Another important development reinforces the same idea.
For years, imaging systems improved by collecting more: more pixels, more frames and more sensor data.
Edge systems are increasingly moving in the opposite direction.
Foveated imaging allocates higher resolution only where it matters. Adaptive frame-rate systems change sensing behavior based on motion or mission state. Intelligent processing pipelines decide which information needs immediate computation and which information can be ignored.
The result is a different definition of sensor intelligence.
A smart sensor is not only one that sees more. It is one that knows what it does not need to see.
This matters especially for small tactical systems, where power, processing capacity and payload are limited.
Every unnecessary frame consumes resources. Every additional sensor adds weight. Every computation competes with another mission function.
Attention therefore becomes part of system architecture.
SWaP is becoming an algorithmic problem
Size, weight and power have traditionally been viewed as hardware constraints.
Computational imaging is changing that.
When optics, algorithms and the mission itself are designed together, SWaP can be addressed at the system level.
The question becomes:
What is the minimum physical sensing architecture required to produce the information the mission actually needs?
This is particularly relevant to small unmanned aerial systems, where relatively small changes in payload weight or power consumption can affect endurance and mission flexibility.
In this context, software is not simply something added to a sensor. It can fundamentally change what sensor hardware is required.
Passive navigation in GNSS-denied environments
Navigation is a good example of this shift.
Traditional navigation architectures may depend heavily on GNSS or other external signals. In a contested environment, those signals can become unavailable, unreliable or deliberately disrupted.
Visual and visual-inertial navigation provide another source of positioning information by extracting motion and location from the environment itself.
When this process can be performed passively, it adds another useful property: the platform does not need to transmit a navigation signal in order to determine where it is.
This principle informs ASIO’s work on NOCTA and GeoFusion™, where visual information is correlated with geographic data to support navigation when GNSS cannot be treated as a constant.
The larger point, however, extends well beyond any one system.
Resilient sensing comes from reducing assumptions about what the environment will provide.
Designing for the contested environment
The future of tactical sensing will not be entirely passive.
Radar, LiDAR, active infrared and other transmitting sensors will continue to provide capabilities that passive systems cannot always reproduce.
The more important change is that active versus passive can no longer be treated as a secondary engineering detail.
It is becoming part of mission architecture.
Future systems will increasingly need to answer several questions at once:
What information does the mission actually require?
What must the system emit to obtain it?
What external signals does it depend on?
How much processing and power does it consume?
And what happens when those assumptions fail?
The strongest sensing architectures will not necessarily be those that collect the most information.
They will be the ones that collect the right information, with the fewest unnecessary dependencies, at the lowest operational cost.
Frequently Asked Questions
What is passive sensing?
Passive sensing uses naturally available energy or signals rather than transmitting energy specifically to make a measurement. Cameras and thermal imagers are common examples.
What is active sensing?
Active sensing transmits energy, such as laser light or radio-frequency signals, and analyzes the returned signal. LiDAR and radar are common active sensing technologies.
Why does passive sensing matter in contested environments?
Passive sensing can reduce the need for active emissions and external infrastructure. Depending on the mission and sensor type, this can help reduce platform signature and improve operational independence.
How can a UAV navigate without GNSS?
Alternative navigation methods include inertial navigation, visual navigation, visual-inertial odometry and correlation of sensed imagery with geographic information. These techniques can provide positioning information when GNSS is unavailable or unreliable.
What does SWaP mean?
SWaP stands for size, weight and power. It is a critical design consideration for tactical and unmanned systems because sensor and compute requirements directly affect payload, endurance and platform performance.
What is visual navigation?
Visual navigation uses cameras and computer-vision algorithms to estimate a platform’s motion or position from features in the surrounding environment, often in combination with inertial sensors or geographic reference data
