Appendix E — Diagnostic Triage for Sensor Faults

Appendix E: Diagnostic Triage for Sensor Faults, Interference, and Deception

Before an anomalous-looking detection is treated as a genuine unknown, it must be assessed against the ordinary explanations that can mimic an anomaly. These fall into three broad families: a) faults internal to the sensor or its processing chain, b) effects imposed by the environment, and c) deliberate adversarial deception. Plus, there is a fourth case that is not an explanation at all: the residue that survives every check. This appendix first defines the categories, then operationalizes them into a triage table mapping each to its characteristic signature, the diagnostic questions that distinguish it, and the corresponding mitigations.

The five definitions below describe the mechanisms by which non-anomalous effects arise. Table 14 reorganizes the same material by operational family for use at the analyst's desk; the mapping is many-to-one rather than exact (for example, "Errors," "Artifacts," and "Interference" are distributed across the table's "Sensor and Pipeline" and "Environment and Propagation" rows), because the definitions are organized by cause while the triage table is organized by the workflow that rules each one out.

• Errors: faults in sensor hardware, calibration, or baselining. Examples: an uncooled thermal imaging sensor experiencing thermal drift that produces distorted object shapes; a radar altimeter with degraded calibration reporting incorrect target ranges.

• Artifacts: false signals generated by the sensing system itself during measurement or processing, which can cause non-physical phenomena to appear as real objects. Examples: cosmic ray hits on CCD/CMOS sensors producing bright pixel anomalies that can be mistaken for point-source objects; lens flare or internal reflections (glare) creating apparent objects in optical and infrared imagery; digital compression or processing artifacts distorting object shape or motion. Aperture-shape artifacts, in which point sources photographed out of focus take on the geometric shape of the lens aperture (often triangular, hexagonal, or octagonal), are a documented source of apparent "structured craft" in civilian footage [60].

• Interference: environmental factors that degrade sensor accuracy. Examples: adverse weather conditions (rain, fog, humidity) reducing optical and infrared sensor performance; electromagnetic interference from solar activity affecting RF sensors; physical clutter in the sensing environment such as birds, debris, or airborne particulates generating false returns.

• Jamming: the intentional emission of RF energy to overwhelm or deny legitimate sensor signals, resulting in degraded detection capability. Examples: GPS or communications jamming in conflict zones. Jamming can make detection and tracking of anomalous objects significantly more difficult in contested electromagnetic environments.

• Spoofing: intentional deception of sensors to cause them to report false data. Examples: broadcasting counterfeit GPS signals to cause receivers to report false locations, altitudes, or speeds; electronic warfare techniques that inject false radar returns, producing "ghost" targets that can make objects appear to perform impossible maneuvers such as instantaneous acceleration or rapid directional changes.

Table 14. Diagnostic triage for characteristic signatures, investigative questions, and mitigations for the principal classes of explainable effect that can masquerade as an anomalous detection, plus the residual case of a genuine unresolved signature.