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.
A US Army soldier uses a GPS receiver. (Photo: US Army)
A modernised military M-Code would be more effective than other tools at securing GPS by resisting adversaries’ jamming, spoofing, misdirection and other EW exploits.
Demand is high for M-Code given its stronger transmission power, broad-spectrum operations and other, classified advantages over Selective Availability Spoofing Module decryption and P(Y) code encryption, to give two examples.
However, the US GAO claimed in a 9 May report that there is a striking imbalance. While 24 of 31 US GPS satellites (described in the report as the ‘aerial sector’) are equipped to broadcast the code, the crucial ‘ground segment’ (control stations and would-be users)
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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.