Cognitive & AI-Enabled RF Systems in Contested Environments Fundamentals Training by Tonex
Adversarial airwaves demand radio systems that learn, reason, and adapt in real time. This course grounds participants in the principles and practice of cognitive and AI-enabled RF across sensing, decision, and control loops under threat. You will connect signal processing with policy-driven adaptation, verification of AI models, and human oversight to ensure mission outcomes. Cybersecurity impact is directly addressed: RF cognition widens the attack surface to data poisoning, model theft, and control-channel spoofing. You’ll learn hardening tactics that fuse EW resilience with secure-by-design AI pipelines, enabling trusted, auditable decisions when spectrum is deceptive and dynamic.
Learning Objectives
- Explain cognitive RF fundamentals and decision loops
- Differentiate classical, model-based, and ML-driven sensing
- Design policy-based adaptation under mission constraints
- Validate and verify AI models for trust and safety
- Integrate human-in-the-loop oversight for accountability
- Apply safeguards where AI, RF, and cybersecurity intersect
Audience
- RF Engineers and Signal Processing Specialists
- Electronic Warfare and ISR Practitioners
- Systems and Test Engineers
- Data Scientists and MLOps Engineers
- Program and Product Managers
- Cybersecurity Professionals
Course Modules
Module 1 – Foundations of Cognitive RF
- Cognitive cycle observe–orient–decide–act
- RF front-end limits and linearity
- Noise, interference, and clutter models
- Knowledge representation for the spectrum
- Onboard vs edge vs cloud inference
- Mission KPIs and safety envelopes
Module 2 – Spectrum Sensing Under Deception
- Threat taxonomy spoofing, replay, mimicry
- Robust energy, cyclostationary, matched filters
- Compressive, cooperative, and distributed sensing
- Adversarial data poisoning and defenses
- Out-of-distribution and novelty detection
- Confidence scoring and decision gating
Module 3 – Policy-Based Adaptive Control
- Mission policies, constraints, and priorities
- Rule engines vs reinforcement learning policies
- Multi-objective reward shaping and safety
- Band selection, power, and waveform agility
- Contention management and deconfliction
- Real-time policy updates and rollback
Module 4 – AI Trust and Verification
- Data lineage, labeling, and drift monitoring
- Model verification, falsification, and stress tests
- Explainability for RF classifiers and estimators
- Robustness to adversarial examples and shifts
- Assurance cases and compliance artifacts
- Runtime monitors, watchdogs, and failsafes
Module 5 – Human-in-the-Loop Operations
- Roles, authorities, and escalation paths
- Operator-centered displays and alerts
- Playbooks for degraded or denied modes
- Calibration of trust and workload management
- Audit trails and post-mission explainability
- Training data feedback from operators
Module 6 – Deployment in Contested Theaters
- SWaP tradeoffs and hardened compute stacks
- Timing, synchronization, and GPS denial
- Networking, control channels, and resilience
- Interoperability with legacy RF systems
- Cybersecurity for models, data, and OTA updates
- Test ranges, emulation, and phased rollout
Advance your mission readiness with trusted, adaptive RF. Enroll your team in Tonex’s Cognitive & AI-Enabled RF Systems Fundamentals to master sensing under deception, policy-based adaptation, AI verification, and human-in-the-loop control—so your systems decide correctly when the spectrum fights back.