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.
Collins Aerospace Systems has received an order from the US Army for PRC-162 software-defined ground radios for the Handheld, Manpack and Small Form Factor (HMS) programme.
This is the sixth order to be placed under the multiple award contract that the army awarded to Rockwell Collins and two other companies in 2016.
The PRC-162 is a two-channel ground radio, both man-portable and vehicle-mountable, that will enable the army to tap into next-generation communications capabilities such as the Department of Defense’s new Mobile User Objective System while maintaining interoperability with legacy waveforms.
The system’s open-architecture design also allows for software-upgradeable capabilities in the future.
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.