Appendix D — Sensor Capabilities

Characterizing an anomalous aerial object means asking two related questions: what do we want to learn about it? and which sensors can tell us? This appendix addresses both. The first part describes the six observable properties that distinguish one class of object from another; the second maps those properties onto specific sensor modalities, with their effective ranges and limitations. The detailed descriptions below expand on each observable property; and Table 13 at the end of this appendix translates them into hardware terms.

Observable Properties

Each observable answers a different question about a target. No single one is decisive on its own; their value comes from combination, since an explanation that satisfies one observable must also be consistent with the others.

Visual-Thermal Signature (What it looks like): Describes the optical and thermal appearance of an object, including its shape, color, size, luminosity, and surface temperature. Electro-optical (EO) cameras operating in the visible band are the most common visual sensors. Infrared (IR) cameras, including forward-looking infrared (FLIR) systems, detect thermal radiation emitted by objects in wavelength bands invisible to the human eye, revealing whether a target is hotter or colder than its background (its thermal or heat signature). This can make thermally distinct objects detectable even when they are visually obscured, though an object at the same temperature as its surroundings may remain undetectable in IR as well. Combined EO/IR systems allow simultaneous visual and thermal observation.

Kinematics (How it is moving): Describes the motion of an object: its position, altitude, velocity, acceleration, heading, and changes in direction. Radar is the primary kinematic sensor, measuring range, bearing, altitude, and radial velocity. LiDAR provides high-precision range measurement using laser pulses. GPS and multi-sensor tracking systems can fuse data to produce continuous three-dimensional flight paths. Kinematic data alone can be highly diagnostic: an object that hovers, reverses direction instantaneously, or sustains accelerations beyond known aerospace tolerances presents a signature that narrows the space of possible explanations. Note that kinematics describes how an object moves; inferring why it moves that way (the forces and energy involved) requires dynamic analysis, which is typically modelled from kinematic data rather than measured directly.

Propulsion Signatures (What is driving it): Sensors can detect secondary indicators of an object's propulsion type, even when the propulsion system itself is not visible. FLIR can identify thermal exhaust plumes characteristic of chemical combustion (jet engines, rockets). Acoustic sensors detect sound generated by propulsion systems and aerodynamic surfaces, jet noise, propeller blade-pass frequencies, and rotor harmonics from helicopters and multirotor drones each produce distinctive acoustic signatures. The absence of expected propulsion signatures (e.g., an object maneuvering with no detectable thermal exhaust, acoustic emission, or aerodynamic control surfaces) is itself a significant observable.

Composition (What it is made of): Describes the material makeup of an object. Hyperspectral imaging sensors measure reflected or emitted light across many narrow spectral bands, producing a spectral signature that can distinguish broad material classes, metals, polymers, ceramics, vegetation, gases, by their characteristic reflectance or absorption features. This is distinct from elemental chemical analysis; hyperspectral sensors classify material types rather than identifying individual elements. Composition data is valuable for discriminating between target types (e.g., metallic aircraft vs. weather balloon vs. atmospheric plasma phenomenon such as ball lightning) when visual or kinematic data alone is ambiguous.

Radio-Frequency Emissions (What signals it is transmitting): Detects active electronic transmissions from a target. Cooperative transponder systems, AIS (Automatic Identification System) for maritime vessels and ADS-B (Automatic Dependent Surveillance–Broadcast) for aircraft, broadcast identity, position, heading, and speed, from which registration and operator information can be queried through external databases. Electronic support measures (ESM) and wideband RF receivers detect non-cooperative emissions such as command-and-control (C2) links between drones and their operators, onboard radar, and telemetry downlinks. The absence of any detectable RF emission from an otherwise trackable object is itself a significant discriminant.

Magnetic Anomaly (What disturbance it produces in the local field): Magnetometers detect deviations from the expected local geomagnetic field caused by ferromagnetic mass (e.g., an aircraft's metal structure), eddy currents induced by moving conductive bodies, or strong local electromagnetic sources. Magnetic anomaly detection (MAD) is an established technique in anti-submarine warfare and geological survey. In the context of UAP investigation, magnetic-field disturbances have been reported in proximity to unidentified targets, though the evidential basis for this correlation remains limited and the causal mechanism, if any, is not established [59].

Sensor Modalities and Limitations

The observables above are abstract; detecting them requires specific instruments, each with its own range, strengths, and blind spots. Table 13 below maps sensor modalities to the evidence they produce and notes, for each, whether it can resolve a representative discrimination problem, e.g., distinguishing a mylar balloon from a genuine unknown.

Table 13. Sensor capabilities and limitations.