CUAS Low Price Sensing Problem – sUAS Information

10 corporations advance scalable, cost-effective options to bolster U.S. counter-sUAS capabilities and meet evolving DOD strategic priorities

 The Protection Innovation Unit (DIU), in shut collaboration with U.S. Northern Command (USNORTHCOM), U.S. Military, U.S. Navy, U.S. Marine Corps, and U.S. Indo-Pacific Command representatives, right now introduced the collection of ten finalists to advance within the Counter-small Unmanned Plane System (C-sUAS) Low-Price Sensing (LCS) problem. These chosen corporations, chosen towards a rigorous set of standards from 118 excellent submissions, will transfer ahead to display their sensor applied sciences throughout USNORTHCOM’s Falcon Peak train in September.

“The evolving menace from unmanned aerial programs calls for that we leverage each instrument at our disposal – from cutting-edge business options to strong partnerships throughout authorities and business,” stated DIU Director Doug Beck. “This problem immediately helps DOD’s strategic priorities by tapping into non-traditional protection corporations to develop scalable, cost-effective sensing options that assist our forces at residence and overseas to establish threats with the pace and constancy wanted to counter them.”

The options chosen characterize a various vary of sensor modalities, together with radio frequency passive detection, energetic radar, acoustic, optical cameras, infrared cameras, and several other combined modality applied sciences. The chosen approaches have the potential to ship important worth in comparison with present DOD programs for key protection areas, at a possible price financial savings of 50-80 p.c in complete price of possession. The ten finalists chosen are:

• BLUEiQ
• CHAOS 1
• Fortem Applied sciences
• Guardian RF
• Hidden Stage
• MatrixSpace
• REVOBEAM
• Squarehead Expertise
• Teledyne FLIR Protection
• Thalrix

Launched in Might 2025, the LCS problem was designed to deal with a vital want: to enhance beautiful sensor programs with reasonably priced, distributed sensing applied sciences that may scale. Defending the homeland from small UAS threats requires broad situational consciousness with attain, redundancy, and resiliency. 

“These ten finalists characterize the forefront of sensor innovation, demonstrating how various technological approaches can improve our layered protection,” stated Director of Replicator 2, David Payne. “Our collaboration with USNORTHCOM, the US Military, and different companions has been instrumental in aligning this effort with the Division’s most important sensing wants. In the end, this prize problem is about serving to to offer the instruments to increase cost-effective sensing capabilities to our most important installations.”

Chosen finalists now advance to reside demonstration and testing at USNORTHCOM’s Falcon Peak 25.2 train. Falcon Peak will present a real-world take a look at of those low-cost sensing options and the prize purse will likely be distributed primarily based on efficiency on the occasion. 

Past the prize cash, high performers may be thought-about for follow-on alternatives, together with Different Transaction (OT) agreements, a Cooperative Analysis and Improvement Settlement (CRADA), and direct transition into operational use, accelerating the deployment of those capabilities and making the perfect tech out there to the warfighter with the pace and scale required.

Matthew Kling, VP and GM of AI Techniques at MatrixSpace, “We’re honored to be a finalist within the @DIU Low-Price Sensing Problem, addressing the pressing have to scale counter-UAS operations, from single websites to nationwide borders. MatrixSpace provides the one commercially out there, distributed radar community for persistent, low-altitude monitoring. Combining superior radar processing, distributed knowledge orchestration and sensor administration, and AI-driven classification, we empower governments and enterprises to safeguard individuals, infrastructure, and nationwide pursuits with unmatched scalability, resilience, and cost-versus-performance.”

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