US Space Force increases efforts to plug training capabilities gaps
The service has been seeking simulation and emulation solutions capable of reproducing multiple in-orbit threats.
Northrop Grumman has received authorisation to proceed with low rate initial production of the Surface Electronic Warfare Improvement Program (SEWIP) Block 3 AN/SLQ-32(V)7 electronic warfare system following a successful Milestone C decision, the company announced on 24 January.
Milestone C is a government led review to assess a programme’s performance and readiness to enter the production and deployment phase.
SEWIP Block 3 is the third in a series of block upgrades of the AN/SLQ-32 electronic warfare system that provides electronic attack capability improvements required to counter the evolving anti-ship missile threat.
SEWIP Block 3 provides improved capability for non-kinetic electronic attack options.
The service has been seeking simulation and emulation solutions capable of reproducing multiple in-orbit threats.
The service has been conducting several acquisition and upgrading efforts involving artificial intelligence and machine learning to improve communication, data analysis and ISR systems.
The Syracuse 4B communications satellite, developed by Airbus and Thales Alenia Space, was launched last year, bolstering secure military satellite communications for the French Armed Forces. Thales has now been selected to provide terminals for vehicles.
The growing importance of space in modern warfare, advancements in satellite technology, and increasing threats from rivals like China and Russia were among the topics of a Eurosatory 2024 panel on military space operations.
AN/ARC-232A is a Starfire radio that provides VHF/UHF communications to airborne platforms and the transceiver is software-programmable, allowing for multiple waveform support as well as optional national electronic counter counter-measure (ECCM) capability.
During the 18-month period of the contract, Lockheed Martin will apply Artificial Intelligence (AI) and Machine Learning (ML) techniques to create surrogate models of aircraft, sensors, electronic warfare and weapons within dynamic and operationally representative environments.