Technical Architecture

System Architecture Overview

Phoenix Rooivalk implements a Comms-Independent Edge Autonomy (CIEA) architecture that achieves sub-2ms authentication and 50–195ms end-to-end decision latency through edge-first processing. The system combines AI-driven threat detection with cryptographically anchored evidence trails to ensure both rapid response and verifiable accountability in all conditions.

Architectural Principles

Edge-First Processing: All critical decisions made locally without network dependency
Byzantine Fault Tolerance: Tolerates up to 1/3 compromised nodes in consensus operations
Modular Design: Swappable components and vendor-agnostic interfaces
Evidence Off-Path: Audit recording doesn't impact real-time performance
Quantum-Resistant: Crypto-agility for post-quantum security requirements

Hardware Foundation: NVIDIA Jetson for Edge AI

NVIDIA Jetson AGX Orin 64GB delivers 275 TOPS of AI performance with 2048 CUDA cores, 64 Tensor cores, and

dedicated Deep Learning Accelerators providing the computational foundation for real-time multi-sensor fusion. The platform achieves 30-60 FPS sustained processing for 4K video streams with sensor-to-decision latency under 50ms using TensorRT optimization.

Performance Specifications

AI Performance: 275 TOPS (AGX Orin), 30-60 FPS sustained processing for 4K video streams Sensor-to-Decision Latency: Under 50ms using TensorRT optimization YOLOv9 Performance: 95.2% mAP with 97.8% precision and 96.5% recall at 30+ FPS Detection Range: 100-500 feet altitude with real-time processing of multiple concurrent streams

Hardware Specifications per Node

Compute: 12-core ARM CPU + 2048-core GPU + dual NVDLA v2.0 accelerators
Memory: 64GB LPDDR5 unified memory (204.8 GB/s bandwidth)
Storage: 512GB NVMe SSD for evidence caching
Network: Dual 10GbE for redundant connectivity
Power: 60W typical, 100W peak consumption
Security: TPM 2.0 module for secure key storage
Operating Temperature: -40°C to +85°C (Industrial variants)
Compliance: MIL-STD-810G shock and vibration compliance

Multi-Sensor Integration Capabilities

Camera Support: Up to 8 MIPI CSI-2 cameras (16 via virtual channels)
LiDAR Integration: 4 lanes PCIe Gen4 for LiDAR and radar sensors
RF Detection: 10GbE networking for RF detection arrays
Acoustic Arrays: 8 I2S interfaces for acoustic sensor arrays
Unified Memory: 204.8 GB/s bandwidth enables real-time fusion of disparate sensor modalities

Core Technology Stack

3. Morpheus (Autonomous AI Decision Engine)

Source: Morpheus Network (mor.org) – decentralized peer-to-peer network of personal AI smart agents

Capabilities

Edge-based threat classification: Real-time decision-making using on-device AI models
Smart contract ROE enforcement: Policy rules encoded as machine-readable constraints
Explainable AI outputs: Confidence scores and rationale for each action
Offline operation: Distributed agent design with no central server dependency
Model versioning: Hash-signed, version-controlled AI model updates

Integration Points

Input: Consumes fused sensor tracks (tracks.v1 format) from sensor fusion layer
Output: Produces engagement decisions (decisions.v1 format) with confidence levels
Human Override: Includes approval channel with full provenance logging

Performance Specifications

Decision Latency: 50-100ms for threat classification and countermeasure selection
Model Size: <50MB optimized models for edge deployment
Confidence Thresholds: Configurable based on ROE and threat level
Update Mechanism: A/B testing in sandbox with HITL approval required

3.1 Solana (Evidence Blockchain Anchoring)

Performance Characteristics

Throughput: 50,000–65,000 TPS sustained in real-world conditions
Finality: ~400ms using Proof of History consensus
Cost: ~$0.00025 USD per evidence anchor
Reliability: Proven mainnet performance with independent validator network

Evidence Architecture

Hash-Chained Batches: Engagement decisions and sensor snapshots hashed and chained together
Merkle Root Storage: Only Merkle roots and indexes stored on-chain
Off-Chain Storage: Full evidence payloads encrypted in Azure Blob Storage or S3
Dual-Chain Option: Etherlink bridge for redundancy and resilience

Technical Specifications for Defense Applications

Ed25519 Cryptographic Signatures: 256-bit security with fast verification optimized for high-throughput
SHA-256 Hashing: Collision-resistant 32-byte fingerprints of evidence
Proof of History: Cryptographically verifiable timestamps establishing tamper-evident chronological ordering
Immutable Programs: Evidence logging logic cannot be altered post-deployment

Legal Admissibility

State Legislation: Vermont, Arizona, and Illinois have enacted explicit legislation recognizing blockchain evidence
Federal Rules of Evidence: Rule 901 (authentication) and Rule 803(6) (business records exception)
International Precedent: China's Supreme People's Court formally recognized blockchain evidence in 2018

3.2 Cognitive Mesh (Multi-Agent Orchestration Framework)

The Cognitive Mesh enables complex coordination among distributed agents (drones, sensors effectors, control nodes) with layers of reasoning and self-optimization.

Architecture Layers

Foundation Layer

Zero-trust security model with continuous authentication
Network abstraction for mesh networking and peer discovery
Edge computing infrastructure management

Reasoning Layer

Multi-sensor data fusion across network-wide inputs
Collective threat assessment algorithms
Pattern recognition and anomaly detection

Metacognitive Layer

Self-monitoring and performance optimization
Real-time parameter adjustment based on outcomes
Sensor health monitoring and reliability weighting

Agency Layer

Task execution and resource deployment
Dynamic role assignment (leader, scout, interceptor, relay)
Coordinated countermeasure execution

Business Applications Layer

External C2 system interfaces
Compliance reporting and user-facing functions
Command center integration and dashboards

Key Components

Agent Registry

Catalog of all drone and sensor agents with capabilities and status
Dynamic role assignment based on tactical situation
Graceful degradation handling when drones are damaged

Hierarchical Decision Confidence Pack (HDCP)

Combines confidence scores from multiple AI agents
Ensemble voting to reduce outlier effects
Target: Improve detection accuracy from 95.0% to 99.5-99.7%

Temporal Decision Core (TDC)

Context enrichment within 50-100ms after authentication
Pattern matching against historical scenarios
Eligibility traces for learning from past incidents

Constraint & Load Engine (CLE)

Dynamic balance of response time vs. accuracy
Resource allocation under high load conditions
Maintains <2ms authentication even under swarm attacks

4.1 Sensor Fusion Layer (Custom Rust Implementation)

Sensor Suite

RF Spectrum Analysis: Protocol-agnostic energy detection and signal fingerprints
Radar Systems: Short-to-mid range sUAS detection with Doppler and micro-Doppler
EO/IR Cameras: Day/night identification with track confirmation and PID support
Acoustic Sensors: Blade-harmonic signatures with urban/forest clutter tolerance
EM Anomaly Detection: Emissions and intent cues in contested RF environments

Processing Pipeline

Real-time track generation: Raw sensor data converted to unified tracks
Feature extraction: Specialized routines for each sensor modality
Sensor calibration: Continuous health monitoring and drift correction
Time synchronization: NTP/hardware clock synchronization across all sensors
Output: Unified tracks.v1 protobuf stream with validated, deduplicated tracks

Performance Targets

Processing Latency: 10-50ms from raw sensor input to fused track
Track Quality: ≥95% precision and recall within operational envelope
Sensor Health: Continuous monitoring with automatic recalibration
Multi-modal Correlation: Cross-sensor validation to suppress false positives

Operational Resilience: GPS-Denied and EW-Contested Environments

Primary GNSS: Multi-constellation (GPS+GLONASS+Galileo+BeiDou)

Galileo: 1m accuracy with free centimeter High Accuracy Service
BeiDou: Two-way messaging and PPP-B2b corrections across 30+ satellites
Terrain-Aided Navigation: High-altitude operations
SLAM/VIO: Low-altitude environments with visual-inertial odometry

Visual-Inertial Odometry Performance

VINS-Mono: Nearly zero drift over 1.62km outdoor paths at 20Hz visual/200Hz IMU
VINS-Fusion: GPU acceleration processing 250Hz on edge devices
Terrain-Aided SLAM: 0.2m final position error over 218km (0.09% of distance)

Electronic Warfare Resilience

Frequency Hopping: Doodle Labs "Sense" technology across 2.4GHz, 5.2GHz, 5.8GHz, 900MHz
Channel Shifting: Microsecond response to jamming detection
Tri-Band Implementation: 15km image transmission under active jamming
Adaptive Filtering: Configurable notch filters rejecting chirp jammers

Pentagon Demonstration 2025 Requirements (March 2025)

Operation from 30MHz-20GHz under active jamming
Low probability of intercept/detect waveforms
Autonomous electromagnetic spectrum maneuvering
Accurate cueing within 2km slant range for Group 1 drones
Autonomous response to EMS impact without operator intervention

Multi-Sensor Fusion Resilience

Sensor Redundancy

Micro-Doppler Radar: 360-degree coverage with rotor signature discrimination
RF Sensors: Passive detection 300MHz-6GHz with protocol analysis
EO/IR Cameras: Visual confirmation and payload identification
Acoustic Sensors: 50-500m range detecting autonomous drones in GPS-denied areas
LiDAR: 64,000 measurements per second with sub-meter accuracy

Mesh Networking Resilience

MANETs: Doodle Labs Mesh Rider multi-band operation M1-M6 (400-2500MHz)
Throughput: Over 100 Mbps with automatic failover routing
MIL-STD Compliance: Tactical band operation with LPI/LPD waveforms
Range: Over 50km with automatic network reconfiguration

Graceful Degradation Strategies

Load Shedding: Drop lower-priority requests under capacity constraints
Multi-Sensor Fusion: Automatic re-weighting when individual units fail
Tiered Response: Fall back from RF jamming to kinetic defeat
Adaptive Thresholds: Dynamic adjustment based on environment and ML optimization

Defense Integration: Lockheed Martin Partnerships and Azure Cloud

Lockheed Martin Integration

Sanctum AI-powered C-UAS Platform (announced February 2025)

Open-architecture approach with modular sensor integration
Cloud computing and multiple effector options
MORFIUS high-powered microwave interceptor for reusable counter-swarm capability
Compatible with M-SHORAD and other DoD architectures

Azure Government Cloud Integration

DoD Impact Level 2-6 Authorizations

FedRAMP High through classified Secret networks
SIPRNet connectivity with exclusive US DoD regions
Physical separation from non-DoD tenants
DISA Provisional Authorizations validated through Lockheed Martin partnership

Edge-to-Cloud Architecture

Azure Stack Edge: Hardware-accelerated ML inferencing at tactical edge
Azure IoT Edge: Zero-touch device provisioning with HSM support
Cloud-Based Command and Control (CBC2): Deployed to all NORAD sectors
Integration: 50+ radar feeds with AI-assisted decision-making

Partnership Development Strategy

Phase 1 (6-12 months): Early-Stage Programs

Technology demonstrations integrating C-UAS data with Azure Government
Target SBIR/STTR or OTA opportunities
Establish supplier diversity relationships with Lockheed Martin early-stage programs

Phase 2 (12-24 months): Teaming Agreements

Execute teaming agreements positioning as specialized C-UAS cloud integration provider
Leverage Azure certifications and defense compliance for competitive advantage
Secure subcontracting opportunities with existing C-UAS prime contractors

Phase 3 (24+ months): Technology Insertion

Achieve technology insertion into programs of record (M-SHORAD, IAMD)
Pursue international partnership opportunities through FMS programs
Transition SBIR/STTR innovations to production contracts