Boeing Contracted for Beowulf MFA Development and Integration
By Richard Scott
Boeing (St Louis, MO) has been funded by the US Naval Air Systems Command (NAVAIR) for engineering and test assets to support of the design, development, and integration of the AN/ALQ-264(V) Beowulf upgrade to the US Navy’s EA-18G Growler airborne electronic attack platform.
As part of the Growler Block II Phase II upgrade, the AN/ALQ-264(V) Beowulf modification introduces an advanced Multi-Function Array (MFA) in the inboard leading edge flaps to augment the functionality and capability of the existing AN/ALQ-218 receiver. Northrop Grumman (Baltimore, Md.) has developed the Beowulf MFA. The MFA also serves as technology development and risk reduction for the incorporation of MFAs on multiple future platforms.
Under a sole-source $34 million contract awarded by NAVAIR in March, Boeing will procure non-recurring engineering and associated test assets, to include four Beowulf A-Kits, four Gunbay Pallet A-Kits, 12 Beowulf B-Kits, 15 sensor control unit B-Kits, and nine power control unit B-Kits. It will also procure support equipment in support of the design, development, and integration of upgrade to the existing EA-18G platform.
The contract value could increase to $489.3 million if all options are exercised. Work is expected to complete in February 2030.
NAVAIR has additionally awarded Boeing a $60.1 million contract modification to support the Growler Block II Phase I upgrade, known as the Next Generation Electronic Attack Unit (NGEAU). These funds cover the procurement of developmental and operational test support, developmental and operational test aircraft installation and capability validation activities, including avionics and airframe material, through to February 2029.
Implementing a so-called Reactive Electronic Attack Measures (REAM) capability, the NGEAU provides a capability that enhances the EA-18G’s ability to autonomously process and respond to unknown signals in an extremely dense electromagnetic spectrum environment. REAM exploits advanced machine learning algorithms to enable effects against agile, adaptive, and unknown hostile radars or radar modes.
