A license plate camera normally answers a simple question: Which car just passed?
But what if it could also help answer who was inside that car and which other people they were traveling with?
That is where a new surveillance technology gets interesting.
Companies are now combining license plate readers with sensors that detect wireless signals coming from nearby devices, including phones and Bluetooth-enabled gadgets. Over time, those signals can form recurring patterns like, the same phone appearing beside the same vehicle, the same group of devices traveling together, or the same electronic signature showing up at different locations.
The technology doesn't need to read your name from your phone. It can potentially learn something more subtle first: that this particular device keeps moving with this particular car.
And once that pattern becomes reliable enough to search, investigators can use it as a lead, even when they didn't start with a person's name, phone number, or license plate.
How a License Plate Camera Starts Recognizing Phones
Phones and other devices are constantly broadcasting wireless signals as they communicate with the world around them.
The trick is connecting those signals to something that investigators already understand.
SignalTrace is designed to work alongside automatic license plate readers. A camera records a vehicle's plate, while nearby sensors can detect electronic signatures from devices traveling around it. Over repeated observations, the system can build a picture of which devices tend to appear together and which vehicles they repeatedly accompany.
Imagine the same car passing a camera every morning with the same phone and smartwatch nearby.
One observation doesn't tell investigators much. But after dozens of trips, that combination starts looking more like a recurring pattern.
The system can then search for that pattern later—even when the license plate isn't visible.
That's a significant shift from traditional license plate surveillance. Instead of searching for a known plate, investigators can potentially start with a recurring electronic pattern and work backward toward the vehicle or person associated with it.
And that's where the technology becomes much more interesting than simply putting another camera on a road.
The Signal Doesn’t Need Your Name to Become Useful
There’s a subtle difference worth understanding, detecting a device is not the same thing as identifying its owner.
A phone's wireless signal doesn't suddenly tell a roadside sensor, “This belongs to John Smith.”
But that doesn't mean the signal is meaningless.
Suppose a particular device keeps appearing near the same vehicle, at roughly the same time, on the same route. Later, that device repeatedly appears outside a particular house. Investigators may already have records connecting that vehicle or address to a person.
None of those individual observations has to contain a name.
The identification can happen later, when separate pieces of information are connected.
This is why the phrase “anonymous device” can be misleading. A random identifier may be anonymous when viewed by itself, but a persistent pattern can make it increasingly distinctive.
Researchers have demonstrated just how revealing movement patterns can be. In one large mobility study involving 1.5 million people, researchers found that just four time-and-location points could uniquely identify 95% of individuals in the dataset.
That study wasn't about SignalTrace, and it doesn't tell us how accurately Leonardo's system can identify a particular person. It does, however, illustrate the underlying problem: you don't necessarily need someone's name when their movements are distinctive enough to single them out.
And once surveillance systems can repeatedly recognize the same signal, the interesting data is what other devices, vehicles, and places keep appearing around it.
But the System Can Find Patterns. It Can't Know What They Mean
This is where the technology gets more complicated.
A recurring electronic signature can be useful without being perfectly reliable. The system can recognize that two devices repeatedly appear together, but it cannot know whether those people are married, coworkers, friends, strangers sharing a ride, or simply happened to be in the same place.
The same problem applies to vehicles.
A phone left inside a parked car can produce a signal that looks like a person staying with that vehicle. Someone borrowing a friend's car can make the opposite pattern. A passenger can also be associated with a vehicle they have no connection to beyond that particular trip.
None of this necessarily makes the technology ineffective. It changes what its output actually represents.
A pattern can be a lead without being proof.
That distinction matters because the technology is designed to make previously difficult searches easier. An investigator who doesn't know which vehicle to look for may be able to search for a recurring electronic signature instead. A pattern that would have been buried in thousands of ordinary observations can suddenly become searchable.
The risk is that once a system highlights a person or group as interesting, that initial inference can influence everything that happens afterward.
The technology doesn't have to be certain to be consequential.
It only has to be useful enough to tell an investigator where to look next.
But a Pattern Can Still Become a Lead
And that's probably the most important part to keep in mind.
SignalTrace doesn't need to tell an investigator, “this is John Smith,” to be useful. It only needs to surface a pattern that would otherwise be difficult to notice.
A recurring device signature might point investigators toward a particular vehicle, location, or group of people. From there, they can use other records and conventional investigative methods to work out what the pattern actually represents.
That makes the technology more like a search tool for relationships hidden inside large amounts of data.
A lead can be useful without being correct. Once a system repeatedly points investigators toward the same device or group, however, that pattern can start influencing which people are investigated, which records are requested, and which events receive closer attention.
What I found interesting is that surveillance systems are becoming better at finding connections between things that were previously collected as separate pieces of information.
A license plate is one record. A wireless signal is another. A location is another. A recurring association between them can turn all three into something much more revealing.
And the more data these systems can connect, the less important it becomes whether any individual piece of information contains your name.
You don't always need to know who someone is to start building a detailed picture of where they go, what they travel with, and who they tend to be around.
That's what makes this technology worth watching because it gives surveillance systems another way to work backward from patterns to people.
