Bennu

Bennu is a configurable acoustic event detection node. Range depends heavily on the mission profile (aircraft sensing, gunshots and impulsive events, wildlife or human presence, infrastructure or perimeter monitoring, and similar CONOPs).

Because acoustic propagation varies significantly by event type, terrain, and environmental conditions, Bennu deployments are mission-tailored.

If you have a specific application in mind, please contact us with your operational requirements, and we will provide a custom range assessment.

Bennu is built for real-world environments, including remote sites, industrial backgrounds, and areas with traffic, aircraft, or weather-driven ambient noise.

To increase accuracy, the system takes advantage of:

• A single low-noise, omnidirectional (360°) microphone
• Advanced signal processing chain + AI-based classification
• A proprietary acoustic signature library

Because target acoustics have distinct spectral and temporal signatures, Bennu differentiates events of interest from ambient noise such as vehicles, wind, and construction activity. Each detection is accompanied by a confidence score, enabling operators or automated systems to make informed decisions.

Detection and classification occur automatically within seconds of acoustic acquisition. A single Bennu node reports event class, confidence, and audio samples; it does not produce per-node directional tracking on its own.

When multiple Bennu nodes are deployed as a sensor mesh, detections can be fused for approximate triangulation and handoff to command-and-control (C2) or downstream systems if required.

A single Bennu unit uses one omnidirectional microphone, so it performs detection and classification rather than single-node bearing/elevation tracking.

In a networked mesh, overlapping detections from multiple Bennu nodes can be used to roughly triangulate a source (including drones). For fixed-site directional tracking with a microphone array, see OSCURA‑120.

No. Bennu is designed to operate autonomously in environments it has not previously encountered.

The system includes:

• AI-based classification with automated calibration
• Adaptive signal processing
• Open architecture integration with existing C2 systems

Because detection is based on acoustic physics and signature recognition rather than static environmental templates, Bennu adapts dynamically to new deployment environments without site-specific retraining. The classification backend is shared with OSCURA‑120.

Yes.

A single Bennu node can operate self-sufficiently to:

• Detect and classify acoustic events onboard
• Output event class, confidence score, and audio samples
• Integrate into existing C2 platforms (TAK, SAPIENT, or custom systems)

For approximate localization, deploy multiple Bennu nodes as a mesh so detections can be fused across the network.

No.

Once deployed, Bennu operates autonomously. It performs detection and classification onboard without requiring continuous operator input. Alerts and event data are transmitted through secure networking channels (LoRa, Wi‑Fi, cellular, or wired).