Multi-Medium Disruption Sensing

Mechanism

The multi-medium sensing primitive is structured around three cooperating components: a heterogeneous sensor admission layer that ingests credentialed observations from any registered medium, a cross-medium coherence evaluator that establishes whether observations from different media are consistent with one another in space and time, and a signature-attribution engine that compares the cross-medium correlation pattern against a credentialed library of disruption signatures. The mechanism's distinctive feature is that the diagnostic decision is taken on the joint pattern across media rather than on any single-medium observation in isolation.

The sensor admission layer accepts observations carrying a standard credentialed-observation structure: signing authority, declared medium, declared modality (the specific sensing technique within the medium), declared uncertainty (typically a calibrated noise model), sensor identifier, sensor pose, temporal scope (the observation interval), and spatial scope (the observable region). The credential is the load-bearing element: a sensor's contribution is admitted on the strength of its credential rather than on the strength of the medium-specific signal alone, so a sensor whose credential has been revoked or whose calibration has lapsed is excluded before its observation enters the coherence evaluator.

The coherence evaluator establishes spatial-temporal alignment across observations from different media. Two observations are considered candidate-coherent if they overlap in space and time within tolerances determined by the sensors' declared uncertainty, and if their declared modalities are physically capable of co-observing the candidate phenomenon. For example, an acoustic observation of a transient event and an optical observation of the same transient must overlap in space (within the propagation envelope of both media), in time (with adjustment for the propagation delay of acoustic versus optical signal), and must both be capable of recording the candidate event. Candidate-coherent observations are passed to the attribution engine; observations that cannot be coherently aligned are retained for single-medium analysis but do not contribute to attribution.

The attribution engine compares the cross-medium correlation pattern against a library of credentialed composite signatures. Each signature describes the expected observable pattern across multiple media when a particular cause — adversarial action, environmental phenomenon, sensor failure, hardware fault, infrastructure malfunction — is operating. The signatures are published by credentialed authorities appropriate to the cause class: spectrum regulators (FCC and equivalents) for radio-frequency adversarial signatures, defense authorities for kinetic-adversarial signatures, meteorological authorities for environmental signatures, geological authorities for seismic signatures, public-health authorities for chemical and biological signatures, and nuclear regulatory authorities for radiological signatures. The engine produces a credentialed attribution observation that names the candidate cause, records the supporting cross-medium evidence, and quantifies the attribution confidence.

Single-medium failure is tolerated by construction. The coherence evaluator and the attribution engine operate on whatever subset of media is currently producing valid observations; a medium that has been jammed, blinded, saturated, or whose sensors have failed simply contributes no candidate-coherent observations. Attribution proceeds on the remaining media with appropriately reduced confidence. The signature library is populated with degraded-medium variants of each canonical signature, so an attribution can be made against a signature that admits the absence of the unavailable medium rather than requiring the full canonical pattern.

Operating Parameters

Practical embodiments must specify the set of admitted media, the modality registry within each medium, the uncertainty model that calibrates each sensor class, the spatial-temporal alignment tolerances applied by the coherence evaluator, the size and structure of the signature library, the confidence-quantification function applied by the attribution engine, and the propagation policy that governs how attribution observations are distributed to downstream consumers.

The canonical embodiment admits nine media: radio frequency (spectrum monitors, software-defined radios, protocol analyzers, radar receivers), optical (cameras, photometric arrays, lidar, hyperspectral imagers), acoustic (microphones, hydrophones, ultrasonic arrays), thermal (forward-looking infrared, infrared cameras, infrared spot sensors), magnetic (magnetometers, fluxgate sensors, coil arrays), seismic (geophones, accelerometers, fiber-optic distributed acoustic sensing), chemical (gas sensors, particle counters, mass spectrometers), biological (immunoassay arrays, DNA-detection sensors, fluorescence-based pathogen detectors), and radiological (Geiger counters, scintillation detectors, neutron detectors). Each medium carries its own modality registry that enumerates the sensing techniques admitted within that medium; new modalities are registered through governance.

Spatial-temporal alignment tolerances are parameterized per modality pair. Optical-acoustic alignment must accommodate the propagation-velocity ratio (acoustic propagation is roughly six orders of magnitude slower than optical), so the temporal tolerance for an event at distance is a function of the propagation delay budget. RF-optical alignment is essentially simultaneous at any sensor scale of interest. Seismic-acoustic alignment must accommodate the differing propagation speeds in solid versus fluid media. The coherence evaluator carries a propagation-physics table that supplies the alignment functions and is updated as new modalities are registered.

The signature library is structured as a credentialed registry keyed by cause class. Typical library sizes in operational embodiments range from hundreds to low thousands of signatures, partitioned among adversarial, environmental, hardware-failure, infrastructure-malfunction, and unknown classes. Each signature includes its canonical pattern (the expected cross-medium correlation when all admitted media are operating), a set of degraded-medium variants (canonical patterns with one or more media absent), and a confidence-quantification function that maps observed correlation strength to attribution confidence. The library is updated as new signatures are published by credentialing authorities; updates propagate through the same governance fabric that admits sensor observations.

Attribution confidence is quantified along multiple axes. The signature-match axis measures how closely the observed pattern matches the canonical (or degraded-variant) signature. The medium-coverage axis measures how many of the canonical signature's required media are currently producing valid observations. The credential-strength axis measures the aggregate credential weight of the contributing sensors. Composite confidence is computed by a confidence-composition function supplied by the operational policy; canonical compositions include component-wise minimum (worst-axis), product (independence assumption), and weighted sum (domain-tuned). Attribution observations carry the full confidence vector so downstream consumers can apply their own composition functions.

Alternative Embodiments

A first embodiment fixes the medium set at the canonical nine. A second embodiment parameterizes the medium set so that domain-specific deployments can omit irrelevant media (radiological is typically irrelevant in agricultural deployments and is omitted) or add domain-specific media (a medical-imaging deployment may add ultrasound and magnetic-resonance modalities). A third embodiment partitions the medium set by deployment tier: edge sensors at a remote installation may admit only RF and optical, while infrastructure sensors at a fixed installation admit all nine.

A fourth embodiment runs the coherence evaluator at a centralized fusion node that receives observations from a sensor network. A fifth embodiment runs the coherence evaluator at edge nodes, where each node fuses the observations within its own observation envelope and propagates only the resulting attribution to upstream consumers. A sixth embodiment is hybrid: edge nodes perform initial coherence evaluation and signature matching, and a centralized node performs cross-edge correlation to detect signatures that span multiple edge envelopes.

A seventh embodiment specializes the attribution engine for adversarial detection in contested electromagnetic environments by emphasizing RF and optical signatures and de-emphasizing the chemical and biological media that are less informative in those environments. An eighth embodiment specializes for chemical-biological-radiological-nuclear (CBRN) defense by emphasizing the chemical, biological, and radiological media and using the RF, optical, and acoustic media for cross-confirmation. A ninth embodiment specializes for critical-infrastructure protection at electric utilities, water and wastewater treatment, port and pipeline, hospital, and transportation operators by registering domain-specific signature libraries that encode the threat model for each vertical.

A tenth embodiment integrates with existing single-medium security tooling by accepting alerts from incumbent intrusion-detection, signal-intelligence, and physical-security systems as observations within the appropriate medium. The alerts are admitted with credentials derived from the incumbent system's identity and contribute to the cross-medium correlation alongside native sensor observations. An eleventh embodiment publishes an attribution feed that downstream consumers (incident response systems, regulatory observers, audit systems) subscribe to.

Composition With Disruption-Modeling Primitives

The multi-medium sensing primitive composes with the broader disruption-modeling primitives disclosed in the Cognition patent. The attribution observation flows into the disruption model as a credentialed cause-identification, allowing downstream behavioral responses (mode change, scope quiesce, evasive maneuver, regulatory escalation) to be conditioned on the cause class rather than on the surface-level observation. Composition with the operating-mode primitive allows mode transitions to be triggered by attribution rather than by raw signal: a transition into a defensive mode requires an attribution to an adversarial cause, while a transition into a degraded mode requires an attribution to an environmental cause.

Composition with the witness primitive allows multi-medium observations to be witnessed by independent peer anchors, raising the credential strength of the resulting attribution. Composition with the scope primitive allows attribution observations to be scoped to the spatial and temporal envelope of their supporting sensors. Composition with the audit primitive allows attribution observations to be retained durably at credentialed audit anchors for post-incident analysis and for signature-library expansion when novel disruptions are observed.

The signature library composes with the broader credential governance fabric: signatures are published by credentialed authorities through the same admissibility evaluator that governs sensor admission, so the library's contents are themselves audited and revocable. A signature that is determined to have been corrupted or to have produced inappropriate attributions can be revoked, and revocation propagates to the attribution engines that have cached the signature.

Prior-Art Posture

Conventional security and disruption-detection systems are predominantly single-medium. Spectrum-monitoring systems detect RF disturbances; physical-security systems detect optical, acoustic, and thermal events; environmental-monitoring systems detect chemical, biological, and radiological events. Each system maintains its own signature library, its own alert pipeline, and its own response chain. Cross-medium correlation, when performed at all, is performed manually by human analysts or by ad-hoc integration scripts that consume alerts from multiple incumbent systems. There is no structural primitive that admits credentialed observations across nine media, evaluates cross-medium coherence under a propagation-physics-aware alignment model, attributes against a credentialed multi-medium signature library, and tolerates single-medium failure by structural design.

Sensor-fusion literature in the autonomous-vehicle and robotics fields addresses cross-modal integration of camera, lidar, radar, and inertial observations for perception purposes, but the fusion is oriented to scene reconstruction rather than disruption attribution and does not integrate with a credentialed signature library or with a governance fabric. Defense-industry multi-INT (multi-intelligence) systems combine signal, imagery, and human intelligence sources but do so within classified workflows that are not exposed as a generic primitive and that do not include the credentialed-observation, scope, witness, and audit substrates of the present disclosure.

Academic work on Byzantine fault-tolerant sensor fusion addresses the problem of tolerating compromised individual sensors but typically treats the sensors as homogeneous and does not address the cross-medium coherence problem in which sensors across heterogeneous physical media must be aligned through propagation physics before their observations can be correlated. The present disclosure is structurally distinct in that it combines the heterogeneous-medium ingestion, the propagation-physics-aware coherence evaluation, the credentialed signature library, the degraded-medium variants for single-medium failure tolerance, and the integration with a broader disruption-modeling fabric as a coherent primitive.

Disclosure Scope

The multi-medium disruption-sensing primitive is disclosed within the Cognition patent as a load-bearing element of the disruption-modeling subsystem. The disclosure scope encompasses the heterogeneous sensor admission layer admitting credentialed observations from the canonical nine media, the cross-medium coherence evaluator with its propagation-physics-aware alignment model, the signature attribution engine with its credentialed signature library and degraded-medium variants, and the propagation policy that distributes attribution observations to downstream disruption-modeling and operational-response consumers.

The disclosure scope contemplates deployment in defense and commercial-drone operations in contested environments, in critical-infrastructure protection at utilities, ports, hospitals, and transportation operators, in autonomous-system fleets requiring adversarial-aware navigation, in chemical-biological-radiological-nuclear defense, and in regulatory-monitoring deployments that must distinguish environmental from adversarial causes for licensing and compliance purposes. Conformance requires that observations carry the canonical credentialed structure, that coherence is evaluated under a propagation-physics-aware alignment model, that attribution is performed against a credentialed signature library, and that the architecture continues to operate when one or more media are unavailable.

The disclosure scope does not require any particular sensor hardware, modality implementation, transport, or signature-library publisher. These are parameterizable within the bounds set by the disclosed semantics. Licensees implementing the multi-medium sensing primitive should expect to provide at least the canonical nine-medium admission layer (or a domain-justified subset), the coherence evaluator with its propagation-physics table, the attribution engine with the canonical signature classes (adversarial, environmental, hardware-failure, infrastructure-malfunction, unknown), and integration with the credentialed-observation, scope, witness, and audit substrates of the broader disruption-modeling architecture. The disclosure scope is independent of the specific physical sensors and operates over any sensor inventory that supports the credentialed-observation model.