Electronic warfare is no longer a niche specialty. It sits at the intersection of RF engineering, software, machine learning, and policy. Over the past several years the U.S. defense establishment has pushed EMSO and spectrum superiority to the top of the priorities list, and civilian applications and threats have followed. If you are an engineer who wants to stay relevant and responsible in 2026, these are the resolutions you should adopt and measure this year.

Resolution 1: Learn the SDR stack end to end and make it reproducible

  • Why: Software defined radio is the baseline testbed for modern EW work. Mastering RF front ends, sampling theory, IF and baseband, digital downconversion, and DSP pipelines is non negotiable. Practical familiarity with mainstream hardware reduces integration risk when you move to FPGA or embedded platforms.
  • Actions: pick one SDR platform (for example an Ettus USRP, a Lime, or a comparable type), and one software toolchain (GNU Radio or a C++/Python stack). Build a reproducible repository with scripts to automate experiments and signal playback. Version control your I/Q datasets and processing blocks.
  • Metric: by the end of quarter one have a documented repository that reproduces a real capture and a matching demodulated output with automated test scripts.

Resolution 2: Get comfortable with FPGAs and hardware acceleration

  • Why: Real-time EW processing often moves from CPU to deterministic hardware. Learning HDL, high level synthesis flows, and how to partition algorithms between FPGA fabric and processors is a force multiplier.
  • Actions: implement a small pipeline in hardware: ADC interface, DDC, decimation, and a simple classifier or detector. Measure latency and resource utilization. Use open toolchains where possible to reduce vendor lock.
  • Metric: produce a working demo that moves one of your GNU Radio blocks to an FPGA and documents latency improvement and resource trade offs.

Resolution 3: Treat ML as an engineering discipline not a toy

  • Why: Cognitive EW and machine learning are moving from papers to contracts and testbeds. Organizations are funding algorithms that classify, cluster, and adapt to unknown emitters in real time. Use ML where it actually solves a problem and be rigorous about training data, labeling, and overfitting.
  • Actions: define a problem narrow enough to solve within six months. Collect controlled I/Q datasets. Start with classical feature engineering and then prototype a neural model. Track performance on held out sets and on adversarial or out-of-distribution captures.
  • Metric: baseline performance on a held-out dataset and a simple adversarial test demonstrating graceful degradation rather than catastrophic failure.

Resolution 4: Build test environments that respect spectrum rules and safety

  • Why: Active RF testing can interfere with critical services and is illegal in many contexts. You must separate lab work from over the air experimentation. The regulatory environment around counter-UAS, jamming, and interception is strict. Private actors are restricted from deploying jammers and mitigation techniques without explicit federal authorization. Follow compliant test methods and coordinate with authorities for live trials.
  • Actions: use RF shielded enclosures, RF attenuators and absorber, anechoic chambers or wired loops to avoid emissions. If you need over the air verification, obtain spectrum authorizations or work with an accredited test range or a government partner. Maintain logs that include time, frequency, power, and point of contact for any tests that could interact with shared spectrum.
  • Metric: all over-the-air tests must have a documented risk assessment and either a spectrum authorization or a signed test range agreement.

Resolution 5: Instrument for repeatability and auditability

  • Why: EW workloads are multi variable and noisy. If you cannot reproduce an experiment, you cannot learn from it. Tracking configuration, firmware versions, amplifier chain, and environmental conditions matters. Audit trails are also necessary if results ever become subject to legal or compliance review.
  • Actions: log all firmware, drivers, OS versions, antenna models, gain settings, and calibration data. Package raw I/Q captures with metadata and checksums. Automate experiment replay where possible so results can be validated by a third party.
  • Metric: any published experiment includes a machine readable manifest that allows an engineer to reproduce the key steps.

Resolution 6: Practice spectrum etiquette and cooperate with spectrum managers

  • Why: Military doctrine and federal strategy emphasize coordinated EMS operations and governance. Spectrum is shared, congested, and contested. Engineers who understand deconfliction, frequency assignment, and electromagnetic battle management concepts are more valuable to operational teams. The Department of Defense has emphasized EMS integration and enterprise governance as essential to modern operations.
  • Actions: learn the basics of spectrum allocation for your region. If you work with public safety bands, coordinate with the relevant authority. When you build proofs of concept, include spectral masks so others can verify you are within limits.
  • Metric: include a spectrum impact statement in any demo that could radiate outside of a shielded environment.

Resolution 7: Harden against electronic and cyber cross effects

  • Why: EW systems are cyber physical. Adversaries will exploit software supply chains, firmware vulnerabilities, and telemetry links. Test and verify the security posture of any connected EW toolchain and assume software updates can change behavior.
  • Actions: use code signing, secure boot where possible, network segmentation in test labs, and basic threat modeling for each build. Record assumptions and mitigations so they are discoverable by operators.
  • Metric: a threat model and mitigation list for each major prototype, reviewed by a peer.

Resolution 8: Engage with policy and legal teams early

  • Why: The law shapes what you can safely test and deploy. Counter-UAS authorities, FAA remote ID rules, and ongoing congressional work on counter UAS mean the compliance landscape is active. Private use of jammers or mitigation tools can have civil and criminal consequences. Engineers who work with counsel and regulators avoid expensive missteps.
  • Actions: for any system that detects or interferes with third party communications, get legal review before any live trials. Track applicable statutes and guidance and document compliance steps.
  • Metric: legal sign off documented before any field test that could affect third party operations.

Resolution 9: Contribute to open reference datasets and safe benchmarks

  • Why: EW needs curated, labeled datasets and community benchmarks to move the field forward in a safe way. Public datasets that are generated in controlled conditions are invaluable for reproducibility and comparison.
  • Actions: publish sanitized I/Q captures with metadata that exclude sensitive content and ensure no unauthorized emissions occurred during capture. Provide baseline code for evaluation and include licensing that restricts misuse.
  • Metric: one dataset and an evaluation script published under a license that enforces responsible use.

Resolution 10: Plan for resilient operations and graceful failure

  • Why: In contested environments you will not get ideal SNR or complete information. Systems should fail predictably and revert to safe modes. Focus on marginal performance, not only peak accuracy.
  • Actions: implement watchdogs, explicit confidence measures, and fallback behaviors. Test algorithms against degraded SNR, frequency hopping, and spoofing scenarios.
  • Metric: documented FMEA style analysis and tests demonstrating safe fallbacks under at least three realistic degraded conditions.

Recommended short reading and entry points

  • DoD Electromagnetic Spectrum Superiority Strategy and implementation material for context on enterprise level priorities.
  • Reports and analyses on EMSO governance and readiness shortfalls to understand where institutional gaps create opportunities for engineers.
  • Recent contracts and programs focusing on cognitive EW to see how practitioners are applying ML to pulse feature extraction and adaptive responses.
  • Practical compliance guides for counter-UAS and C-UAS legal restrictions so you know the red lines for private and municipal deployments.

Practical rhythm to keep yourself honest

  • Monthly: capture one new signal class and add it to your dataset. Document capture conditions.
  • Quarterly: deliver a small integration milestone. Q1 SDR pipeline reproducible repo. Q2 FPGA acceleration demo. Q3 ML baseline validated on held out data. Q4 integrated pipeline with safety and legal sign offs for an authorized test range.
  • Yearly: publish one safe dataset, one white paper on lessons learned, and a public postmortem on one failed experiment and what you learned.

Final note on ethics and responsibility Electronic warfare carries moral and legal responsibility. Tactical successes that ignore safety, civilian impact, or legal frameworks produce strategic liabilities. Engineers should aim to build capabilities that are robust, auditable, and designed to fail safely. That combination of technical rigor and prudence will make your work useful to operators while keeping you out of regulatory trouble and legal risk.

If you adopt these resolutions you will improve as an engineer and raise the quality of the EW community. Start small, measure everything, and make safety and compliance a feature of your engineering practice, not an afterthought.