BLADE-EDGE Governance Node

National Importance

Autonomous defense systems — directed-energy weapons, interceptor drones, and swarm platforms — can react in milliseconds, far faster than any human operator. As these systems proliferate, the critical question shifts from "can they act?" to "should they act?"

Current autonomous systems face critical governance challenges: no standardized hardware governance layer exists (most rely on software-only rules that can be bypassed or fail silently), single-sensor systems are vulnerable to spoofing and deception, individual platforms make isolated decisions without cross-validating with friendly units, and cloud-based governance introduces unacceptable latency for tactical engagements.

The BLADE-EDGE Governance Node directly addresses DoD Directive 3000.09, which requires "appropriate levels of human judgment" in autonomous weapon systems. This device implements that requirement at the hardware level — with a physically hardwired normally-open safety interlock that literally cannot close unless the full 9-module governance pipeline confirms authority.

Research Problem

Autonomous defense systems face a fundamental governance challenge: they can react in milliseconds, but no standardized hardware exists to enforce rules of engagement at machine speed. Current approaches rely on software-only governance that can be bypassed, corrupted, or fail silently under adversarial conditions.

The BLADE-EDGE Governance Node addresses five specific capability gaps:

This platform demonstrates that authority-governed autonomy can be implemented at the hardware level for defense applications, with physically enforced safety interlocks, multi-sensor cross-validation, and swarm consensus — capabilities that software-only approaches cannot guarantee.

Directed-Energy Weapon Governance Platform

The BLADE-EDGE extends the authority-governed autonomy framework to directed-energy weapon systems, where engagement decisions must account for beam-specific factors that kinetic weapons do not face. The final authority computation includes a beam suitability scalar (β_beam) that gates the pipeline output based on physical engagement feasibility.

This extension demonstrates that authority-governed autonomy is not limited to navigation or surveillance systems but can govern lethal engagement decisions where the governance layer must operate at machine speed while maintaining human-judgment requirements.

System Overview

The BLADE-EDGE receives data from six sensor categories, processes it through a 9-module governance pipeline on dual-redundant compute hardware, and outputs one of four decisions: EXECUTE, DELAY, ABORT, or HANDOFF.

9-Module Governance Pipeline

Every engagement decision passes through nine sequential modules targeting 50-300ms end-to-end latency. Each stage can independently trigger an ABORT, preventing downstream execution.

Authority Decision Model

The BLADE-EDGE outputs one of four decisions based on pipeline authority and beam suitability. Unlike binary fire/no-fire systems, the governance model provides graduated response with automatic effector handoff.

The safety interlock relay is physically hardwired normally-open — the weapon literally cannot fire unless the BLADE-EDGE actively confirms full authority through all six pipeline stages. This is hardware-enforced governance, not software-only.

Sensor-Anchored Trust Assessment (SATA)

The BLADE-EDGE implements SATA trust evaluation across six sensor categories simultaneously. Each feed is independently evaluated for signal integrity, noise floor, expected pattern consistency, and cross-correlation with other sensors. The output is a single trust scalar τ ∈ [0,1].

Trust(s_i) = weighted belief function with beam-path confidence integration.
Cross-sensor validation: radar + EO/IR + LIDAR + GPS/IMU + atmospheric.
A degraded GPS signal reduces τ even if radar is strong.
Atmospheric sensors feed directly into trust computation (beam propagation feasibility).

Key innovation: beam-path confidence is integrated at the trust layer rather than post-pipeline. Atmospheric sensors measuring humidity, obscurants, and turbulence feed directly into SATA because a clear sensor picture is useless if the directed-energy beam cannot reach the target. This tight coupling between sensing and beam physics is unique to BLADE-EDGE.

Hardware Architecture

72 components across 8 subsystems with full dual-redundancy on all critical paths.

Compute Subsystem ($10,948)

Sensor Subsystem ($47,480)

Communications ($47,500)

Power and Redundancy Architecture

Every critical subsystem has a backup. The system continues operating with any single component failure. Power is fully dual-redundant with OR-diode isolation, battery backup for 5-minute graceful shutdown, and supercapacitor ride-through for power transients.

Redundancy Matrix

Power Flow

Three-Layer Security

Bill of Materials: $138,908

The top 3 cost drivers (GPS + radios + encryptors) account for 56% of the BOM. These are military-controlled items whose cost decreases significantly in production quantities. Production units (100+) estimated at $60-80K per unit.

Physical Specifications

System Schematic

72 components, 103 electrical connections (53 power + 50 data). Full dual-redundancy on all critical paths.

Governance Simulation Environment

The BLADE-EDGE simulation environment executes the complete 9-module governance pipeline in real-time with configurable scenarios, multi-target tracking, and heterogeneous effector assignment. The simulator mirrors the hardware pipeline architecture, enabling direct validation of governance behavior before physical prototype fabrication.

This simulation demonstrates executable validation of the defense-grade governance pipeline — not conceptual design alone. Every decision visible in the simulator corresponds to the exact same pipeline logic that would execute on the Jetson AGX Orin + Zynq UltraScale+ hardware.

Research Simulation Environment (v5.0.3). This environment validates governance behavior across all 9 pipeline modules with real-time multi-target tracking and heterogeneous effector assignment. All computations run client-side in the browser.

Role in the Governance Stack

The BLADE-EDGE Governance Node represents the defense-grade implementation of the authority-governed autonomy framework. All seven governance architectures (SATA, HMAA, ADARA, MAIVA, FLAME, CARA, ERAM) are deployed in hardware form within this platform, extending the research from simulation to a production-specification system designed for real-world contested environments.

Validation Metrics

Experimental Program

Seven planned experiments designed to validate the BLADE-EDGE governance pipeline under increasingly adversarial conditions. Each experiment measures decision correctness, latency compliance, and safety interlock behavior.

1. 1Single-Sensor Spoofing: Inject false LIDAR returns while radar and EO/IR remain clean. Measure SATA cross-validation penalty and trust decay rate.
2. 2GPS Spoofing Detection: Inject false GPS coordinates. Verify ADARA detects GPS-IMU disagreement and triggers authority reduction.
3. 3Swarm Consensus Failure: Compromise one BLADE-EDGE node in a multi-unit mesh. Verify MAIVA Byzantine consensus rejects the compromised unit.
4. 4Atmospheric Degradation: Simulate turbulence and obscurant conditions. Verify beam suitability scalar β_beam drops and system issues HANDOFF to kinetic effector.
5. 5FLAME Micro-Deliberation: Create ambiguous target conditions. Measure deliberation window timing (50-300ms) and verify re-query of ADARA and MAIVA before decision.
6. 6Compound Attack: Simultaneous GPS spoofing + LIDAR injection + atmospheric degradation. Verify pipeline issues ABORT, safety interlock remains open, CARA recovery activates.
7. 7End-to-End Recovery: Full scenario — compound attack, trust collapse, ABORT, CARA recovery, sensor revalidation, trust restoration, mission resumption. Measure total recovery latency.

Each experiment targets minimum 30 trials for statistical significance. Metrics: decision correctness, end-to-end latency, safety interlock state, false positive rate, recovery time.

Project Status

Project Documentation

Complete engineering documentation for the BLADE-EDGE Governance Node. All files are original work by Burak Oktenli.

Reproducible Research Artifacts

This project provides complete reproducible artifacts enabling independent verification of the system design, component selection, and governance architecture.

Future Work

The BLADE-EDGE design specification represents the first phase. Future work focuses on physical realization and operational validation.

SDK Integration

The BLADE Governance SDK provides a unified API across all four domains. The same blade_governance library drives defense weapons governance (BLADE-EDGE), autonomous vehicle authority (BLADE-AV), maritime surveillance (BLADE-MARITIME), and critical infrastructure protection (BLADE-INFRA). Only the domain configuration file changes.

domain: defense
pipeline: SATA → ADARA → IFF → HMAA → MAIVA → FLAME → CARA → BDA → EFFECTOR

sensors:
  - id: radar_track
    type: phased_array_radar
    weight: 0.30
    cross_validate: [eo_ir, lidar_ranger]
  - id: eo_ir
    type: electro_optical_infrared
    weight: 0.25
    cross_validate: [radar_track, lidar_ranger]
  - id: lidar_ranger
    type: beam_path_lidar
    weight: 0.25
    cross_validate: [radar_track, eo_ir]
  - id: iff_transponder
    type: mode_5_crypto
    weight: 0.20
    cross_validate: [radar_track]

effector:
  type: directed_energy_weapon
  relay: normally_open_interlock
  safety_standard: DoDD_3000_09
  engagement_gate: HOTL_required
  fail_safe: beam_disable_on_loss

authority:
  A3_threshold: 0.80  # Full engagement authority
  A2_threshold: 0.55  # Tracking only, no engagement
  A1_threshold: 0.30  # Passive monitoring
  A0_action: beam_safe  # Hardware interlock opens
  hysteresis_up_s: 10
  hysteresis_down_s: 0  # Immediate downgrade

import blade_governance as bg

# Initialize with defense domain config
pipeline = bg.GovernancePipeline("blade_edge.yaml")

# In your weapon system control loop (100Hz):
while mission_active:
    sensors = get_sensor_readings()
    
    result = pipeline.evaluate(sensors)
    # result.trust         → 0.87
    # result.authority     → "A3"
    # result.deception_p   → 0.04
    # result.flame_hold_ms → 0
    # result.execute       → True
    
    if result.execute:
        effector.engage(result.envelope)
    elif result.authority == "A0":
        pipeline.cara_recover()  # GREP phases

# ROS 2 Topic Map — Defense
/blade/sata/fused_trust          # Float64 τ ∈ [0,1]
/blade/hmaa/authority_level      # UInt8 {A3,A2,A1,A0}
/blade/hmaa/command_envelope     # EnvelopeMsg
/blade/flame/circuit_breaker     # UInt8 state
/blade/flame/deliberation_ms     # UInt32 hold time
/blade/cara/grep_phase           # String {GUARD,REDUCE,EVALUATE,PROMOTE}
/blade/effector/interlock_state  # Bool (relay open/closed)
/blade/adara/deception_prob      # Float64 P(adversarial)
/blade/bda/revalidation          # TrustRevalidation

# Core API — domain-agnostic
pipeline = bg.GovernancePipeline(config)
result   = pipeline.evaluate(sensors)
recovery = pipeline.cara_recover()

# result object — universal fields
result.trust          # Float64  τ ∈ [0,1]
result.authority      # String   {A3,A2,A1,A0}
result.deception_p    # Float64  P(adversarial)
result.flame_hold_ms  # UInt32   deliberation window
result.execute        # Bool     action permitted
result.relay_state    # Bool     hardware interlock
result.grep_phase     # String   CARA state

# Lifecycle
pipeline.get_audit_chain()   # Hash-chained log
pipeline.export_forensics()  # BLADE-BLACKBOX
pipeline.get_config()        # Current domain cfg

About This Project

The BLADE-EDGE Governance Node is part of the authority-governed autonomy research program by Burak Oktenli at Georgetown University (M.P.S. Applied Intelligence). It demonstrates mastery across 12 technical domains: GPU-accelerated edge AI, FPGA real-time processing, multi-sensor fusion, military standard compliance, hardware security architecture, distributed consensus, thermal management, power systems engineering, EMI/EMP hardening, real-time operating systems, autonomous systems ethics, and defense systems integration.

Related research architectures: SATA (sensor trust), HMAA (authority computation), CARA (recovery), MAIVA (multi-agent trust), FLAME (latency control), ADARA (deception-aware risk).

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