Artificial Intelligence Helps Protect Troops in Denied GPS Environments

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By Brooks McKinney, APR

Military units operating in austere environments rely on satellite-generated GPS signals to ensure the accuracy of their position, navigation and timing (PNT) equipment. Unfortunately, adversaries often seek to degrade or deny warfighters' access to these GPS signals using low-level jamming or spoofing techniques. These disruptions can render navigation equipment inaccurate, leaving both the warfighters and their missions at risk.

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Combating Threats with Software Innovation

Northrop Grumman experts in PNT are using Artificial Intelligence (AI) and Machine Learning (ML) to develop a new way to combat these threats to navigation systems using innovative software techniques.

"Our new machine-learning-based GPS threat detector is a set of software designed to be deployed on a variety of software-defined radios and other types of embedded systems," explained Brent Bateman, a senior staff engineer at Northrop Grumman. "It rapidly detects and classifies low-power GPS threats that have traditionally been very difficult to observe."

An embedded system is a microprocessor-based hardware and software system that performs a dedicated function as part of a larger mechanical or electronic system. It is typically constrained in terms of its size, weight and power consumption. Embedded systems are typically found in automobiles, consumer electronics, aircraft and military electronic systems.

Northrop Grumman initially developed the technology using its own independent research and development (IR&D) funding but then transitioned the work to a contract R&D project with the U.S. Army. To date, the technology has been demonstrated at PNTAX, a military field test involving Army ground vehicles.

Neutralizing the Spoofers

According to Dr. Jeff Dickman, director of future PNT systems at Northrop Grumman, the GPS threat detector software scans the radio frequency (RF) signal environment for indicators that adversaries are trying to jam or spoof GPS signals.

"If an enemy jammer is present, that can cause unprotected navigation systems to lose lock on the GPS signal," he explained. "If we don't have lock, we'll lose our knowledge of position and time, and that will disrupt our mission."

Enemy attempts to spoof GPS signals are even more disruptive, he added, because unprotected warfighters may not realize that an adversary is present and that the GPS signals they're using may not be accurate or trustworthy.

"Our GPS threat detector is designed to analyze the threat environment, look at the observables and say, 'Based on what we're seeing, we think there's a threat out there jamming or manipulating your incoming GPS signal,'" said Dickman.

He explained that once a threat is detected and identified, the GPS threat detector is designed to share its knowledge of the threat type and location with a larger network of users who can then decide how to react to it. The algorithm also uses the threat data to automatically update its own list of known threats to look for in subsequent scans of the RF environment.

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According to Bateman, the artificial intelligence algorithm used by the GPS threat detector software derives from Northrop Grumman's PNT expertise using machine learning technology and enemy threat conditions and techniques.

"Our algorithm uses machine learning to search for threat characteristics within vast amounts of data," he said. "The goal is to detect and identify threats in the observed RF environment so that intelligent navigation systems can take action to protect themselves, either by reconfiguring their own operation or by coordinating with friendly forces to neutralize the threat."

The addition of this technology to the threat detector algorithm, he added, was shown to improve its ability to detect "hard-to-detect" threats, such as a low-power signals near the noise floor of RF receivers, and decrease the number of false-positive threats.

Bateman emphasized that Northrop Grumman's new algorithm should be viewed not as a "new" machine-learning technique but rather as a novel application of the company's expertise in artificial intelligence developed to solve a very difficult navigation warfare (NavWar) problem facing today's modern warfighter.

According to Eva Baron, program manager at Northrop Grumman, based on new data collected and received real-time, they are able to generate and send back to the field a new model to detect previously undetected threats in the theater all within minutes, effectively demonstrating edge analytics. "Our first field-tested PNT and ML integration demonstrated significant improvements in threat detection and we are just scratching the surface," she said. The company is currently investing more into improving PNT ML models to further aide navigation warfare.

A Disruptive Force in the Future of PNT Systems

Another powerful characteristic of the GPS threat detector software — and one requested by the company's PNT Army customer — is that it's designed to be hosted on small, low-cost, low-power, military-grade embedded systems. Such systems must be optimized to perform their function well while minimizing computationally intensive calculations and algorithms.

"Our threat detector operates well in low data bandwidth environments while retaining its ability to detect key threat characteristics," said Bateman. "This characteristic allows its algorithm to be successful in very constrained operating environments."

Though still in development, Bateman emphasized that Northrop Grumman's GPS threat detector software promises to be a disruptive force in the future of PNT systems.

"This technology was designed from the ground up to be a software-defined algorithm that can be rapidly modified, retrained and redeployed in the field," he said. "It promises to not only reduce the development time and implementation costs of new PNT capabilities but also improve their ability to keep pace with constantly evolving NavWar threats.