Adding Machine Learning to Protect Intelligent Navigation Systems
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.
