Electronic jamming detection and analysis software

Q: How does sweep jamming differ from responsive or reactive jamming?

Sweep jamming (also called spot-sweep) moves a high-power spot jammer rapidly across a frequency range, spending brief dwell time on each channel. In a waterfall display it appears as a diagonal or repeating sweep pattern. A fast sweep can deny all channels in a band if the sweep rate is faster than the target's frequency agility or re-acquisition time. Responsive (reactive) jamming is more sophisticated: the jammer listens for transmissions and only activates on the exact frequency and at the moment it detects a signal from the target. This maximizes jamming efficiency and minimizes the jammer's own RF footprint, making it harder to detect and locate. Reactive jamming is particularly challenging because the jammer is silent when the target is silent, so energy detection alone may miss it — cyclostationary analysis or protocol-aware detection methods are needed.

By Corvus Intelligence Engineering Team · About the team →

June 25, 2026 11 min read

An adversary who controls the RF spectrum controls the battlefield. Electronic jamming — deliberately radiating RF energy to degrade, deny, or deceive victim receivers — is one of the oldest and most persistent threats in modern warfare. The challenge for the defender is that jamming is often invisible until its effect is already felt: a link goes silent, a data feed stalls, a GPS position freezes. Electronic jamming detection software changes that dynamic by turning passive victims into informed responders, identifying what type of jamming is occurring, where the jammer is, whether it is hostile or a friendly fratricide event, and what countermeasures are most likely to restore link quality. This article covers the full detection and analysis pipeline — from jamming type signatures and signal quality monitoring, through spectral anomaly detection and jammer geolocation, to automated response recommendations and C2 integration.

Jamming types and their signal signatures

Not all jamming looks the same in the spectrum, and correctly identifying the type is the first step toward an effective response. The five principal jamming categories each leave a distinct spectral and temporal fingerprint.

Barrage jamming floods a wide frequency band — potentially tens or hundreds of megahertz — with noise or structured interference. In a power spectral density display the noise floor rises across the entire protected band, sometimes by 20–40 dB. The jammer's power is spread over the full bandwidth, so the power spectral density at any individual channel is relatively low, but every channel in the band is simultaneously denied. Barrage jamming is the blunt instrument of EW: easy to detect, hard to attribute precisely, and expensive in terms of jammer power per affected channel.

Spot jamming concentrates the full transmitter power on a single narrow channel or frequency. In the spectrum it appears as a sharp, high-power spike with anomalously elevated received signal strength at the target frequency — often 30–50 dB above the expected signal level. Spot jamming achieves the highest jamming-to-signal ratio per watt of radiated power, making it the most efficient technique against a target known to operate on a fixed frequency. Its weakness is that a frequency-agile target can simply hop away.

Sweep jamming sweeps a high-power spot across a range of frequencies at a controlled repetition rate. In a waterfall (time-frequency) display it appears as a diagonal stripe or repeating comb pattern, spending milliseconds to seconds on each channel before moving on. The dwell time per channel determines whether it disrupts reception: if the sweep rate is faster than the target's re-acquisition time, each channel is effectively jammed even though the jammer is not there continuously.

Pulse jamming radiates high-power bursts at a fixed or varying pulse repetition interval, exploiting windows between the victim's own transmit slots or targeting specific protocol timing. In the time domain it appears as periodic power spikes; in the frequency domain as spectral lines at the pulse repetition frequency and its harmonics. Pulse jamming is common against radar and TDMA communications systems where timing structure can be exploited.

Responsive (reactive) jamming is the most sophisticated and operationally challenging type. The jammer monitors the spectrum for transmissions from the target and activates only when a victim signal is detected — often with a latency of microseconds to milliseconds depending on the sensing and response hardware. In a spectrum recording, reactive jamming is identifiable by its tight temporal correlation with victim link activity: the jammer appears and disappears in lockstep with the transmitter it is attacking. Simple energy detection often misses reactive jamming entirely because the jammer is silent during monitoring intervals when the target is not transmitting.

Jamming type	Bandwidth	Spectral signature	Temporal pattern
Barrage	Wide (tens–hundreds MHz)	Elevated noise floor across band	Continuous
Spot	Narrow (channel-width)	Sharp high-power spike	Continuous
Sweep	Variable (swept range)	Moving spike / diagonal waterfall	Periodic sweep
Pulse	Narrow to wide	PRI spectral lines	Pulsed at fixed PRI
Reactive	Matches victim channel	Correlated with victim Tx	On/off tracking victim

Received signal quality monitoring

The most direct evidence of jamming is not what appears in the spectrum but what disappears from the communication link. Signal quality monitoring (SQM) instruments the receiver side of every protected link and raises alerts when key metrics fall outside their operational envelope.

SNR degradation detection is the foundational metric. Every receiver reports a measured signal-to-noise ratio, and the SQM engine maintains a per-link, per-channel baseline. When observed SNR drops more than a configured margin below baseline — typically 3–6 dB for a yellow alert, 10+ dB for a red alert — a detection event is raised. The rate of SNR decline matters as much as the absolute drop: a gradual fade may indicate propagation, while a sudden step-drop correlated with no change in weather or link geometry strongly suggests jamming onset.

Link quality indicator (LQI) monitoring captures the protocol-layer view. LQI aggregates bit error rate, packet error rate, retransmission counts, and receiver lock status into a composite score, often standardized to a 0–100 or 0–255 scale depending on the radio type. LQI degradation often precedes complete link failure and gives the detection engine a second, protocol-independent indicator of interference. Critically, LQI monitoring catches jamming types that may not yet be visible as elevated spectral power — reactive jamming that injects errors into specific packet fields, for example.

Jamming-to-signal ratio (J/S) estimation is the most operationally informative metric because it predicts how much power the victim would need to increase (or how many dB of frequency separation are needed) to restore the link. The SQM engine estimates J/S by comparing the received interference power in the affected channel against the measured signal level from the legitimate transmitter. A J/S of 0 dB means the jammer and the intended signal arrive with equal power; for a narrowband voice link, that is already disruptive. Spread-spectrum waveforms can tolerate positive J/S values (the jammer is stronger than the signal) and only break at J/S above the processing gain. Publishing J/S estimates per link allows the response engine to rank countermeasure options by the margin they are likely to recover.

Key insight: A jammer that degrades link quality but stays below the energy-detection threshold is the hardest adversary to catch. Combining three independent indicators — spectral power elevation, LQI degradation, and J/S ratio estimation — and cross-correlating their timing reduces the false-negative rate substantially compared to any single metric alone.

Spectral anomaly detection algorithms

Raw energy detection — comparing received power against a static threshold — was the state of the art for jamming detection two decades ago. Modern electronic warfare environments require three layers of increasingly sophisticated algorithms stacked on top of each other.

Energy detection remains the first and fastest layer. The detector compares the received power spectral density in each channel against an adaptive threshold derived from the noise floor estimate. The threshold adapts to slow environmental changes — different times of day, different terrain, equipment aging — using a constant false alarm rate (CFAR) algorithm that maintains a fixed probability of false alarm as the background shifts. Energy detection latency is milliseconds and its compute cost is trivial, making it the natural candidate for triggering the more expensive second-layer analysis.

Cyclostationary feature analysis exploits the periodicity hidden inside modulated signals. Natural thermal noise and most natural RF background sources are wide-sense stationary — their statistical properties do not vary in time. Virtually all engineered communication and jamming signals, however, have statistical moments that repeat at their modulation rate, symbol period, or chip rate. These cyclic features appear as peaks in the spectral autocorrelation function (SAF) at nonzero cycle frequencies. A reactive jammer that bursts only when the victim transmits has a cyclic feature at the victim's packet repetition frequency. A chirp jammer has a feature at its sweep repetition rate. Cyclostationary detection is computationally heavier than energy detection — typically implemented on GPU or FPGA to meet latency requirements — but it achieves detection of structured jamming 10–20 dB below energy-detection thresholds while maintaining the same false-alarm rate.

ML-based anomaly detection addresses the threat that neither energy detection nor the cyclostationary library has ever seen before. Adversaries adapt: a sophisticated opponent who knows the defender's detection library will design jamming waveforms that exploit gaps in it. An anomaly detection model trained on large volumes of clean-spectrum and known-interference data learns the statistical manifold of the expected RF environment. Any spectrum snapshot that falls outside that manifold — regardless of its specific properties — triggers an alert. Deep learning architectures, particularly convolutional networks operating on spectrogram images and autoencoders operating on raw IQ features, have demonstrated strong performance on novel jamming waveform detection without requiring explicit characterization of the jamming signal. These models require periodic retraining as the baseline environment evolves, and they should always be paired with a human analyst review workflow so that novel detections are investigated rather than just logged.

Jammer geolocation: TDOA/FDOA with multi-sensor networks

Detecting that jamming is occurring is a prerequisite for situational awareness; knowing where the jammer is enables action. Jammer geolocation converts the detected emission into a geographic position fix using passive multi-sensor techniques — no active interrogation of the jammer is required.

The geolocation method depends on the available sensor geometry and the jammer's emission type. For persistent jammers with continuous or near-continuous emission, TDOA/FDOA is the primary technique. Multiple receiving sensors, distributed across the area of operations and synchronized in time and frequency, all receive the jamming signal simultaneously. The engine cross-correlates the signals from each pair of sensors to extract the time difference of arrival (TDOA) — the lag at which the jammer's signal, received at sensor A, best aligns with the version received at sensor B. Each TDOA measurement constrains the jammer to a hyperboloid surface; collecting measurements from multiple sensor pairs and solving the overdetermined system with a weighted least-squares estimator collapses the constraints to a point fix with an associated confidence ellipse.

FDOA — frequency difference of arrival, arising from relative motion between sensors or between jammer and sensors — provides a complementary constraint. Isodoppler surfaces are geometrically orthogonal to TDOA hyperboloids, meaning that the dimensions poorly constrained by TDOA are often well-constrained by FDOA and vice versa. This orthogonality is particularly valuable for barrage jamming: the wideband noise emission produces an excellent cross-ambiguity function peak (because bandwidth determines timing resolution), making barrage jammers paradoxically easier to geolocate than the narrowband spot jammers that deliver higher J/S. See our detailed treatment of passive geolocation TDOA FDOA for the full mathematical framework.

Position uncertainty ellipse is not optional output — it is a required deliverable. A position fix without an error bound is operationally misleading. The uncertainty ellipse is computed from the solver's covariance matrix, which combines measurement noise (derived from the cross-ambiguity function peak sharpness) with the geometric dilution of precision (GDOP) for the sensor configuration. A well-distributed sensor network with good geometry relative to the jammer produces a compact, nearly circular ellipse; a poor geometry — sensors strung along a line with the jammer off one end — produces an elongated ellipse spanning kilometers in the worst direction. Publishing both the fix and its ellipse allows downstream consumers, including EW officers and targeting planners, to make risk-appropriate decisions about how much confidence to place in the position.

Sensor geometry optimization is an active planning function. Given a jammer detection event and the current disposition of receivers, the geolocation engine can compute how GDOP would change if a specific sensor were repositioned or if an additional platform were added to the network. This output feeds into sensor tasking decisions: an unmanned aerial vehicle carrying a receiver node can be directed to a location that minimizes the uncertainty ellipse for the current jammer position estimate. The geometry computation is fast enough to run continuously in the background, providing updated sensor movement recommendations as the tactical picture evolves.

Jamming classification and intent assessment

A detection event that correctly identifies the jamming type and estimates the jammer's location still does not tell the commander whether this is an adversary action requiring a tactical response, a fratricide event requiring deconfliction with friendly EW assets, or natural interference requiring only a frequency change. Intent assessment closes that gap.

Distinguishing intentional jamming from interference relies on several converging indicators. Deliberate jamming tends to be spectrally structured — it may carry the fingerprint of a known jammer design, have modulation characteristics inconsistent with natural RF noise, or exhibit cyclostationary features tied to the victim's own transmission timing. Natural interference from industrial equipment, atmospheric disturbances, or adjacent-channel intermodulation products has different spectral and temporal characteristics. The classification engine assigns a probability of intentional origin based on a multi-feature discriminant that combines spectral shape, temporal pattern, J/S level, and correlation with victim link activity.

Hostile versus fratricide assessment requires spatial correlation. The jammer's estimated location, with its uncertainty ellipse, is compared against the known positions of friendly EW assets — aircraft carrying electronic attack pods, vehicle-mounted jammers in the convoy, range trainers conducting scheduled EW exercises. If the geolocation fix is consistent with a friendly platform position within the ellipse uncertainty, the system flags a fratricide probability and alerts the EW coordination cell before escalating to an adversary threat designation. Getting this wrong in either direction has serious consequences — failing to flag fratricide wastes countermeasure resources and may trigger friendly-force retargeting, while failing to flag a hostile jammer leaves a genuine threat unaddressed.

Correlation with threat intelligence is the final layer. The classification engine queries the ELINT threat library for emitter parameter sets matching the detected jamming: frequency, bandwidth, power levels, modulation type, sweep characteristics. A strong parameter match against a known threat emitter type raises the hostile intent score and provides context — what platform typically carries this jammer, what its typical operating range is, what its known countermeasures are. Even a partial match narrows the analyst's attribution task from an unconstrained search to a targeted investigation. The full fusion of ELINT and COMINT data in threat classification is addressed in our article on cognitive electronic warfare software.

Automated response recommendations

Intent assessment produces a threat record; the response engine converts that record into ranked, executable countermeasure options. Automation at this stage is not about removing human judgment from the decision — it is about ensuring that the human decision-maker has the right options presented in the right order within the tight time windows that tactical jamming creates.

Frequency hop suggestions are the most common immediate countermeasure. The response engine queries the spectrum deconfliction for military database to identify channels in the protected band that are currently unaffected by the jamming, have sufficient signal margin for the required link quality, and are deconflicted from other friendly users. Candidate channels are ranked by estimated J/S margin on the new frequency, factoring in the jammer type — if it is barrage jamming, no frequency within the jammed band will be clean, and the recommendation may be to move entirely outside the band or switch to a different communication path.

Power adjustment recommendations estimate how much transmit power increase would be needed to restore link margin at the current frequency, given the estimated J/S ratio. The engine computes the required power delta against the platform's transmit power budget and flags whether restoration is feasible without exceeding power limits or causing new interference to adjacent friendly links. If the required power increase would cause unacceptable interference, the recommendation is downgraded in favor of frequency or path changes.

Alternative communication paths are recommended when neither frequency hop nor power increase can recover the link. The response engine has visibility into the overall network topology — available satellite communication windows, HF relay paths, nearby mesh nodes not in the jammed sector, fiber or wired fallback — and recommends specific alternative paths ranked by estimated capacity and latency against the mission communication requirements. For time-sensitive traffic, the recommendation includes a latency estimate for each path so the operator can make an informed trade between capacity and delay.

Escalation to EW officer is triggered when the jamming crosses defined thresholds: estimated hostile intent above a probability threshold, J/S high enough that no available countermeasure can restore the link, jammer position fix sufficiently precise to support EA targeting, or jamming affecting a link category flagged as mission-critical. The escalation includes the full event record — detection time, jamming type, J/S, geolocation fix with ellipse, intent score, evidence chain, and the countermeasure options already exhausted or ruled out — packaged to give the EW officer everything needed to make a targeting or reprogramming decision without having to dig through raw data.

Integration with electronic warfare and C2 systems

Jamming detection does not live in isolation. Its value multiplies when the detection and response pipeline is tightly integrated with the wider EW and command-and-control architecture.

Feeding jamming tracks to the common operating picture is the most immediate integration requirement. Detected jamming events, with their geolocation fixes and confidence ellipses, are formatted as electronic warfare tracks and pushed to the COP via the appropriate protocol — TAK/CoT for dismounted and mounted forces using Android Team Awareness Kit, Link 16 for aviation and maritime platforms, or a REST/MQTT broker for software-defined networks. The COP overlay shows the jammer as a threat icon with its uncertainty ellipse, the affected friendly links highlighted, the jammer type label, and the current J/S estimate per link. Operators see, at a glance, which frequencies and links are under attack and where the threat is located relative to the force disposition.

Coordination with EA assets involves passing the jammer track — with its geolocation fix, velocity estimate if tracking, and classification — to electronic attack planners. EA assets, whether airborne jammers, directed energy platforms, or fires, need precise targeting data; the geolocation fix with its confidence ellipse directly informs the EA platform's targeting solution. The integration also runs in the reverse direction: when an EA asset takes action against a jammer, the detection engine monitors the affected frequencies to confirm suppression or to detect that the jammer has changed frequency or location — closing the EW kill chain with feedback.

EW reprogramming triggers connect the detection pipeline to the platform's EW reprogramming workflow. When a jamming event matches a known threat in the library with high confidence, the detection record is passed to the reprogramming system with the matched threat identifier and the observed parameters. If the observed parameters differ from the library values — for example, a known threat jammer is now operating at a shifted frequency or with a different waveform — the delta is flagged as a potential threat evolution requiring a library update. When the jamming does not match any known threat, the detection record is automatically submitted as a candidate new threat for intelligence exploitation. This feedback loop ensures that the detection library grows with operational experience rather than stagnating against a static threat picture.

The architecture that ties these components together — sensor networks, signal processing pipelines, geolocation engines, COP feeds, and EW coordination interfaces — is the same layered SIGINT and EW platform that handles all RF intelligence tasks. Building that architecture with open interfaces ensures that a new jamming detection capability can be added as a microservice without rebuilding the data flows that already exist for spectrum deconfliction, signal classification, and emitter tracking.

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Corvus SENSE integrates multi-layer jamming detection — energy detection, cyclostationary analysis, and ML anomaly models — with TDOA/FDOA geolocation and automated response recommendations, feeding every event directly into your common operating picture.

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This analysis was prepared by Corvus Intelligence engineers who build mission-critical electronic warfare detection and SIGINT software for defense and government organizations. Learn about our team →

Frequently Asked Questions

What is electronic jamming detection software?

Electronic jamming detection software is a specialized signal processing application that continuously monitors the RF spectrum and communications links for the presence of deliberate interference. It ingests receiver measurements — signal power, noise floor, SNR, LQI, and spectral content — and applies energy detection, cyclostationary analysis, and machine learning algorithms to identify when and where jamming is occurring. Once jamming is detected, the software classifies the type (barrage, spot, sweep, pulse, or reactive), estimates the jammer's location using multi-sensor geolocation, and outputs both threat alerts and recommended countermeasures. In a military context it feeds directly into the electronic order of battle and common operating picture, giving commanders real-time EW situational awareness.

What is the difference between barrage jamming and spot jamming?

Barrage jamming floods a wide swath of spectrum with noise or interference — often tens or hundreds of megahertz — to deny use of any channel in that band simultaneously. It is effective regardless of which frequency the target is using, but the jammer must spread its power across the full band, reducing the power spectral density at any given channel. Spot jamming concentrates the full jammer power on one specific frequency or narrow channel, achieving a very high jamming-to-signal ratio against that single target. It is more power-efficient but requires the jammer to know which frequency to attack. In spectral data, barrage jamming appears as a broad, elevated noise floor across many channels; spot jamming appears as a sharp, narrowband spike with anomalously high power at a specific frequency.

How does sweep jamming differ from responsive or reactive jamming?

Sweep jamming moves a high-power spot jammer rapidly across a frequency range, spending brief dwell time on each channel. In a waterfall display it appears as a diagonal or repeating sweep pattern. A fast sweep can deny all channels in a band if the sweep rate is faster than the target's frequency agility or re-acquisition time. Responsive (reactive) jamming is more sophisticated: the jammer listens for transmissions and only activates on the exact frequency and at the moment it detects a signal from the target. This maximizes jamming efficiency and minimizes the jammer's own RF footprint, making it harder to detect and locate. Reactive jamming is particularly challenging because the jammer is silent when the target is silent, so energy detection alone may miss it — cyclostationary analysis or protocol-aware detection methods are needed.

What is the jamming-to-signal ratio and why does it matter?

The jamming-to-signal ratio (J/S) is the ratio of the jammer power received at the victim receiver to the legitimate signal power received from the intended transmitter, usually expressed in decibels. It is the fundamental metric governing how much damage jamming inflicts. A J/S of 0 dB means jammer and signal arrive with equal power; the minimum J/S needed to disrupt a link depends on the modulation, coding, and receiver design — a spread-spectrum CDMA link may tolerate +10 to +20 dB before losing lock, while a narrowband AM voice channel breaks at near 0 dB. Jamming detection software estimates J/S from the received noise floor and signal level, and uses it both to assess severity and to recommend the minimum power adjustment or frequency change needed to restore the link margin. Continuous J/S monitoring also distinguishes mild interference from deliberate high-power jamming.

How is a jammer located using TDOA and FDOA techniques?

Jammer geolocation works the same way as passive emitter geolocation for any RF source. Multiple receiving sensors, synchronized in time and frequency, simultaneously receive the jammer's emission. The time difference of arrival (TDOA) between each sensor pair — measured by cross-correlating the received signals — constrains the jammer to a hyperboloid surface. If the sensors are moving, the frequency difference of arrival (FDOA) provides an additional isodoppler constraint orthogonal to the TDOA surface. A weighted least-squares or maximum-likelihood solver intersects the constraints from all sensor pairs to produce a position estimate with a confidence ellipse. The geolocation accuracy depends on sensor synchronization quality, signal bandwidth, geometry, and the jammer's emission type — a barrage jammer broadcasting wideband noise is actually easier to locate via cross-correlation than a narrowband spot jammer, because wideband signals give sharper cross-ambiguity function peaks.

Can jamming detection software distinguish intentional jamming from unintentional interference?

Yes, though it requires analysis beyond simple energy detection. Several signal characteristics help distinguish deliberate jamming from natural or accidental interference. Deliberate jamming tends to be correlated with the activity of the target — it appears or intensifies precisely when the victim link is active (reactive jamming), or it persistently occupies frequencies assigned to military networks. The spectral shape matters too: structured jamming waveforms (chirps, barrage noise with specific modulation, deceptive repeater signals) have cyclostationary features absent from thermal noise or incidental interference from industrial equipment. Spatial correlation across sensors can also flag jamming: if an anomalous signal appears simultaneously across multiple widely separated receivers, it is unlikely to be local interference. Finally, correlation with threat intelligence — known adversary jammer frequencies, known hostile actor TTPs — raises or lowers the probability of hostile intent. All these factors are combined into an intent score by a classification engine.

What automated countermeasures can jamming detection software recommend?

Automated response recommendations from a jamming detection engine fall into three tiers. Immediate link-layer responses include frequency hop to a clear channel (with the specific target frequency and the estimated J/S on alternative channels ranked by availability from the spectrum deconfliction database), increase transmit power to improve the signal-to-jammer ratio, switch to a more jam-resistant waveform, or activate spread-spectrum or frequency-hopping mode if not already in use. Network-level responses include rerouting traffic via alternative communication paths — satellite, HF relay, or a mesh node not affected by the jammer — and adjusting network topology to isolate the affected node. Escalation responses flag the event for human decision: notify the EW officer, contribute the jammer location fix to the electronic order of battle for potential EA tasking, or trigger an EW reprogramming request if the jamming matches a threat whose countermeasure library needs updating.

What is cyclostationary feature analysis in jamming detection?

Cyclostationary feature analysis exploits the fact that most man-made signals — including many jamming waveforms — have statistical properties that vary periodically at their modulation rates, symbol rates, or chip rates. Unlike thermal noise, which is wide-sense stationary, a BPSK jamming signal has a spectral correlation feature at twice the carrier frequency; a chirp jammer has a cyclic feature at its sweep repetition frequency; a pulsed jammer exhibits spectral lines at its pulse repetition interval. The cyclic spectral density (the spectral autocorrelation function evaluated at nonzero cycle frequencies) reveals these features even when the signal is buried in noise and invisible to simple energy detection. In jamming detection, cyclostationary analysis allows the system to detect low-power reactive jammers that are silent much of the time, identify the modulation type of a jamming signal to assist classification, and distinguish structured jamming from thermal noise with a much lower probability of false alarm than energy detectors.

How does jamming detection software integrate with a common operating picture?

Integration with the common operating picture (COP) happens through standardized data feeds and overlay layers. Detected jamming events are formatted as electronic warfare tracks — with frequency, estimated power, location fix and confidence ellipse, classification, and timestamp — and pushed to the COP platform via TAK/CoT protocol, Link 16, or a REST/MQTT data broker depending on the network architecture. On the COP, the jammer appears as a geospatial threat icon with an uncertainty ellipse, overlaid on the tactical map alongside friendly forces, sensor coverage footprints, and communication links. The COP operator can see which friendly links are being degraded, the jammer's estimated location and heading if it is mobile, and the recommended response. Jamming tracks are also correlated with the electronic order of battle database: if the jammer matches a known threat emitter in the ELINT library, the association is displayed, giving the commander context about the platform likely operating it.

How do machine learning models improve jamming detection over threshold-based methods?

Traditional energy-detection jamming detectors compare received power or SNR against a fixed or adaptive threshold. This works well for strong, persistent jamming but fails for low-power reactive jammers, novel waveforms not in the detection library, and environments with rapidly varying natural interference. Machine learning improves detection in three ways. First, ML models trained on large spectral datasets learn complex, non-linear features that distinguish jamming signatures from legitimate signals and natural noise, achieving lower false-alarm rates at given detection probabilities. Second, anomaly detection models trained only on the clean-spectrum baseline can flag novel jamming waveforms the system has never seen before — a critical capability against adaptive adversaries who change their jamming technique. Third, recurrent or transformer-based models track the temporal evolution of spectrum features, recognizing reactive jamming by its time-correlation with victim transmissions even when each individual event is below the energy-detection threshold.