Protecting bases and critical infrastructure from unmanned aerial systems (UAS)requires more than blind faith in radar.

One commander, convinced his radar could handle any threat, learned this the hard way when a drone slipped beneath detection by flying low to the ground—delivering a photo of his front door. Radar has limits, and adversaries know how to exploit them.

That’s where audio changes the game. Unlike radar or cameras, acoustic sensing works through darkness, fog, smoke, and terrain. It can’t be beaten by flying lower or hiding behind obstacles, making it a powerful layer of defense.

Funded by AFRL, Certus Innovations' ACE-ML effort builds on this principle, creating a distributed, phone-based detection network that transforms every smartphone into part of a counter-UAS shield. Using purpose-built datasets and advanced machine learning, ACE-ML can 1. detect, 2. classify, and 3. localize drones in real time.

Field testing has already proven its effectiveness, distinguishing seven drone types at ranges over 600 feet with a 91% success rate. The approach eliminates single points of failure by self-forming and self-healing, meaning the network grows stronger as more devices join.

The value is clear:

ACE-ML delivers cost-efficient, scalable protection without requiring massive radar installations or vulnerable camera systems. Soldiers, security personnel, or even civilians already carrying phones instantly become sensors, expanding coverage without additional burden. As one advocate put it, “If the guy in the watchtower already has a phone, why wouldn’t I want him to be a sensor? It doesn’t have to be the best sensor—the alternative is no sensor.” For defense, homeland security, and industry, this represents a paradigm shift in force protection—an early warning system that is resilient, distributed, and built for the realities of 21st-century threats.