by Kris Osborn
The Army is working quickly with industry to pursue massive, far-reaching weapons integration with electronic warfare (EW) systems to improve defenses, prevent enemy jamming and identify and disrupt enemy communications.
Part of bringing this to fruition is related to ongoing Army work to rapidly improve EW weapons by drawing upon increased artificial intelligence (AI) and machine learning. One Raytheon effort, developed to align with Army requirements, uses analytics and advanced automation to organize, detect, emit and thwart a complex array of electronic signatures. The program, called Electronic Warfare Planning and Management Tool (EWPMT), seeks to address and advance the Army’s interest in AI-driven EW and spectrum management.
Raytheon’s growing emphasis upon EW has been leveraged for many years now, as the firm worked on the Navy’s next-generation Jammer program, a new multi-frequency EW weapon intended to empower maritime air attack with jamming and anti-jam technology. This emphasis is also seen in a number of recent Raytheon business deals, including a recent $500 million deal between Raytheon and Cobham Advanced Electronic Solutions.
“Cobham will provide electronic warfare systems and aerospace support for Raytheon Technologies’ key missile, radar and space programs. The agreement supports 12 programs within Raytheon Missiles & Defense, Raytheon Intelligence & Space, and Collins Aerospace businesses,” a Raytheon statement said.
The technical improvements to the EW system are being delivered in specific upgraded software increments, Raytheon developers said. “EWPMT allows soldiers to plan and synchronize electronic warfare. It enables the spectrum manager to do what they do by looking at real-time spectrum interference based on sensor data,” Niraj Srivastava, product line manager for multi-domain battle management, Raytheon SAS, told reporters last year.
“There can be up to 100 different things for an Electronic Warfare Operator (EWO) to manually type in—which can now be automated,” Srivastava said. “It helps you determine what to jam and what not to jam and connect with sensors on a network.”
In effect, advanced machine learning and automation can massively expedite what is called Spectrum Frequency Allocation Formats, or technologies intended to discern necessary EW threats. As a software system, Srivastava explained, EWPMT can accommodate a wide range of sensors with factors such as soldiers’ backpacks and vehicles; it can also be adopted and customized for Navy and Air Force systems as well.
EWPMT allows soldiers to visually “synergize its EW attack, targeting and surveillance capabilities,” according to an Army report. The concept, being brought to fruition by advanced software, is to fuse and integrate otherwise disparate pools of sensor data, frequency information, targeting intelligence and EW signatures into a single, integrated picture.
Given that there is an increasingly crowded and “finite” amount of spectrum, Army weapons developers have had long-standing concerns about EW threats. Interestingly, a 2006 essay from The Department of the Army seems to foreshadow, if not predict, emerging contemporary EW defense challenges.
“The spectrum is a resource, and while non-expendable, it is finite. A limited number of channels, or frequencies, can be accommodated at any given time in a given area. While it is true that emerging systems are more efficient users of bandwidth they also use more bandwidth to pass larger amounts of data which leads to frequency congestion because never before have so many emitters been present in an area of operations,” the essay, titled Army Electromagnetic Spectrum Management Operations, states.
No comments:
Post a Comment