Maryland Police Deploy SignalTrace System to Track Devices Linked to License Plates

Key Takeaways

• Leonardo’s ELSAG SignalTrace is designed to detect wireless device signals and correlate them with automatic license plate reader data.
• The system can identify recurring combinations of phones, Bluetooth devices, wearables, vehicle systems and other wireless devices as an “electronic fingerprint.”
• SignalTrace does not need to read the contents of messages or communications to create useful investigative patterns.
• The technology has important technical limitations: wireless devices do not necessarily transmit stable identifiers continuously, and sensors must capture signals at the appropriate time and frequency.
• Repeatedly associating the same collection of devices with a vehicle, location and other observations could make an otherwise unidentified device pattern increasingly identifiable.
• The Supreme Court’s 2026 decision in Chatrie v. United States held that police acquisition of cellphone location history from Google is a Fourth Amendment search, although the Court left the ultimate reasonableness of the warrant for the lower court to decide.

What SignalTrace Actually Does

SignalTrace is not a conventional license plate reader. It is an electronic signal intelligence system developed by Leonardo and marketed through its ELSAG law-enforcement technology business. According to Leonardo’s official SignalTrace product documentation, the system detects identifiers associated with Bluetooth devices, Wi-Fi sources, RFID tags, mobile phones, wearables, vehicle components, infotainment systems and other connected devices.

The technology is designed to supplement automatic license plate recognition rather than simply replace it. SignalTrace can correlate device observations with ALPR information, including a license plate, location and timestamp, and group devices that repeatedly travel together into what Leonardo calls an “electronic fingerprint.”

That distinction matters. A conventional ALPR system primarily answers a question such as where a particular vehicle’s license plate was observed. SignalTrace is designed to add another layer of information by asking which electronic devices appear to be associated with that vehicle or movement pattern.

Leonardo says SignalTrace captures publicly emitted signal activity and does not decrypt or read the contents of devices or communications. The system therefore does not need access to a person’s text messages, photographs or other stored content to create correlations from the identifiers and signals it detects.

How SignalTrace Can Link Devices to Vehicles

The basic concept is relatively straightforward. Imagine a vehicle repeatedly passing a sensor while carrying a smartphone, smartwatch and wireless device. If the same combination of signals is observed traveling together over multiple encounters, the system can build a recurring electronic pattern.

When an ALPR camera simultaneously records a license plate, the device pattern can potentially be associated with that vehicle. Leonardo says the technology can help recognize vehicles through their electronic fingerprint even when a visible or correct license plate is unavailable.

This does not mean every passing phone can automatically be identified by name. SignalTrace works with device signals and correlations rather than directly receiving a person’s identity. Turning an electronic fingerprint into a named individual would generally require additional information or investigative steps.

Technical Limitations

SignalTrace’s capabilities should not be confused with continuous, GPS-style tracking of every nearby smartphone. Wireless devices do not necessarily transmit a stable, uniquely identifiable signal at every moment, and different technologies operate across different frequencies and protocols.

That means a sensor has to detect a relevant transmission when it is listening in the appropriate way. Environmental conditions, device settings, signal strength, radio behavior and the physical position of the device can all affect what a sensor observes.

Device identifiers can also be randomized or otherwise changed depending on the technology and operating system. Consequently, a single observation does not necessarily create a permanent identifier that can be followed indefinitely.

These limitations are important because they mean SignalTrace should not be described as an all-seeing system capable of continuously tracking every phone in range. Its practical value comes from collecting observations over time and correlating recurring patterns.

Anonymous Doesn’t Necessarily Mean Unidentifiable

A device signature does not have to contain a person’s name to become useful as an investigative lead. Repeated observations can gradually connect an otherwise unidentified device pattern with places, vehicles and other devices.

For example, if the same group of wireless devices repeatedly appears with a particular vehicle, and that vehicle is repeatedly observed at a particular residence, investigators could potentially use other records to determine who is associated with the pattern.

Research into mobility data has demonstrated how powerful seemingly anonymous movement information can be. A 2013 study published in Scientific Reports analyzed mobility data from 1.5 million people and found that four spatiotemporal points were sufficient to uniquely identify 95% of individuals in the dataset.

That study involved cellphone mobility data rather than SignalTrace and should not be interpreted as proof that SignalTrace can identify 95% of people. It does, however, illustrate the broader privacy issue: repeated location and movement patterns can become identifying when combined with other information.

Surveillance Potential

SignalTrace’s current limitations do not necessarily define its long-term surveillance potential. The system’s usefulness could increase as agencies deploy more sensors, collect observations over longer periods and improve algorithms for correlating device patterns.

Leonardo’s own product material says SignalTrace can operate with or without co-located ALPR cameras and can be deployed in environments such as rail stations, event venues and shopping centers. That broadens the potential use case beyond traditional roadside license-plate monitoring.

The privacy concern is therefore less about a single sensor instantly revealing someone’s identity and more about the accumulation of repeated observations. A database containing recurring associations between devices, vehicles, locations and timestamps could potentially provide investigators with a detailed picture of movement and association without needing to read the content of the devices themselves.

What the Supreme Court’s 2026 Geofence Decision Means

The legal landscape around electronic location information also changed in 2026. In Chatrie v. United States, the U.S. Supreme Court held that police conduct a Fourth Amendment search when they obtain a person’s cellphone location information from Google.

In its June 29, 2026 opinion, the Court held that people have a reasonable expectation of privacy in records concerning their cellphone location. The case involved a geofence warrant that required Google to provide location information for phones located near a Virginia credit union during a specified period.

The decision does not directly determine whether SignalTrace’s collection of publicly emitted wireless signals is constitutional. SignalTrace involves a different technology and data source than Google’s stored Location History. The Supreme Court also did not decide whether the specific geofence warrant in Chatrie was ultimately reasonable under the Fourth Amendment, leaving that question to the lower court.

Still, the ruling adds an important legal development to the broader debate over law-enforcement access to digitally generated location information. As surveillance systems become capable of linking more types of electronic identifiers with physical movements, questions about warrants, privacy expectations, data retention and government access are likely to become increasingly important.

Frequently Asked Questions

Q: What is SignalTrace?

A: SignalTrace is an electronic signal intelligence system from Leonardo’s ELSAG business. It is designed to detect publicly emitted signals from devices such as Bluetooth equipment, Wi-Fi sources, RFID devices, mobile phones, wearables and vehicle electronics, then correlate those observations with vehicles and ALPR data.

Q: How does SignalTrace work?

A: SignalTrace detects wireless device identifiers and groups devices that repeatedly travel together into an electronic fingerprint. When ALPR data is available, those device patterns can be correlated with a license plate, timestamp and location.

Q: Can SignalTrace read my texts or phone contents?

A: Leonardo says SignalTrace does not decrypt or read the contents of devices or communications. Its advertised function is to capture publicly emitted signal activity and correlate device identifiers and patterns.

Q: Can SignalTrace continuously track a phone?

A: Not in the same way as a GPS tracker. Wireless devices do not continuously transmit stable, identifiable signals, and detection depends on the technology, transmission behavior and whether a sensor captures the relevant signal. Repeated observations can nevertheless create useful movement patterns.

Q: Can SignalTrace identify a person?

A: SignalTrace does not necessarily identify a person by name from a single signal. However, recurring device patterns can potentially be associated with vehicles, locations and other information, creating investigative leads that may help identify the person using or carrying those devices.

Q: Did the Supreme Court ban geofence warrants?

A: No. In Chatrie v. United States, the Supreme Court held that police acquisition of cellphone location information from Google constitutes a Fourth Amendment search. The Court did not decide whether the particular geofence warrant was reasonable and sent that issue back for further proceedings.