A new F-35 on-board computer system has now fielded and taken to the skies, an upgraded logistics and maintenance database system intended to ensure safe, efficient flight and information management on board the jet.
The system, called Operational Data Integrated Network (ODIN), was developed as a follow-on or improvement upgrade to the F-35s well known Autonomic Logistics Information System (ALIS). ODIN is slated to be fully operational by 2022, a Lockheed statement said.
This kind of high-speed, diagnostic computer system performs a number of critical functions. One of those is condition-based maintenance wherein onboard sensors and computers monitor flight systems such as engine rotations or cooling functions. In addition, ODIN will examine the component health of on-board software and hardware throughout the aircraft such as avionics and other electronics. Part of the concept is to anticipate potential failures well before there is any kind of malfunction to both preserve the safety and survivability of the aircraft and also streamline the repair and maintenance process by getting ahead of the curve. Most of all, a diagnostic or predictive computer system of this kind can mitigate the risk of any kind of in-flight malfunction which could of course introduce substantial performance, functionality and even tactical complications and problems.
The ODIN system, however, is not likely restricted to purely maintenance functions but also plays a vital role in aircraft information processing, management, and transmission. The F-35 is widely regarded as being at the forefront of emerging AI systems, meaning its sensor fusion applications began as mere concepts years ago. Now otherwise disparate pools or streams of information such as targeting, navigational details, threat data, weather conditions, and basic flight trajectory details can all be gathered, analyzed, organized, and presented on a single screen to F-35 pilots.
While this process has been underway now for many years along with the evolution of F-35 technologies, a former Air Force Chief Scientist told me several years ago that it would indeed be correct to view F-35 sensor fusion as an early iteration of AI.
Given this, it is a fair estimation that logistical and procedural data are closely interwoven with sensor information, threat specifics and other kinds of crucial in-flight details. An AI-enabled machine could, for instance, assess engine performance and calculate thrust or acceleration as it pertains to the pace at which an F-35 might close with an enemy or approach a target area for a close-air-support mission. Readiness and aircraft system health and performance parameters are, it would seem, closely connected to other vital on-board computer processing systems.
AI-enabled algorithms can gather sensor data and input from on-board systems, bounce incoming information against a vast database of compiled specifics, technical details and previous circumstances to make discernments, perform analytics and render fast, useful solutions for pilots and maintainers. “Mission capable rates for the aircraft continued to improve in 2020 with rates greater than 70% across the fleet, and even higher for deployed units,” the Lockheed statement adds.
Information sharing, data aggregation, sensor input organization and real-time analytics are all key factors contributing to the F-35s performance in recent Joint All-Domain Operations exercises. Those war games aim to practice how the stealth fighters gathers and networks combat relevant details across multiple air, sea and land domains at one time, the Lockheed essay says.
Kris Osborn is the defense editor for the National Interest. Osborn previously served at the Pentagon as a Highly Qualified Expert with the Office of the Assistant Secretary of the Army—Acquisition, Logistics & Technology. Osborn has also worked as an anchor and on-air military specialist at national TV networks. He has appeared as a guest military expert on Fox News, MSNBC, The Military Channel, and The History Channel. He also has a Masters Degree in Comparative Literature from Columbia University.