Vetronics systems today are increasingly focused on C4ISR [command, control, communications, computers, intelligence, surveillance, and reconnaissance], interoperability with other battlefield elements, and the capability to be optionally unmanned if necessary. They are also more likely to be built on open architectures as the U.S. Department of Defense (DoD) continues the push toward a modular open systems approach (MOSA) in upgrades and new designs.

A team of researchers at the Johns Hopkins Applied Physics Laboratory (APL – Laurel, Maryland) is developing a system to monitor physical fatigue and possible injury in soldiers in near-real time using body-worn sensors and machine learning (ML) algorithms.

For many years, military-system designers relied solely on direct attached storage (DAS) devices when they needed to deploy data storage on military platforms. These devices are embedded within or directly attached to the computers, such as single-board computers (SBC) on a platform.

Radar and electronic warfare (EW) are challenging applications for designers of test systems. The only constant seems to be change in technologies, tactics, and countermeasures. Industry players believe that artificial intelligence (AI) may hold the key to driving the effectiveness of radar and EW.

Powered by artificial intelligence (AI), a massive military Internet of Things (IoT) promises a host of battlefield benefits in such areas as unmanned surveillance and targeting, situational awareness, soldier health monitoring, and other critical applications. However, major data and communications challenges must be overcome first.

Now more than ever, federal agencies within the U.S. Department of Defense (DoD) must develop software capabilities that are compatible with legacy technology while maintaining and meeting strict security needs that protect proprietary code and networks.

Due to the agility of military equipment, the use of embedded computer modules (SOM) to create drone computing brains, that can be upgraded with visual computing requirements, will bring greater design flexibility and better system reliability.

This article details performance measurements vs. predictions of a 16-chan- nel S-band direct sampling receiver design. The design is based on recently released direct sampling analog-to-digital converters (ADCs) clocked at 4 GSPS and sampling in the second Nyquist zone of the converter.