A Timestamp Without Uncertainty Is Not Enough

Introduction

An event timestamp is only as trustworthy as the uncertainty that accompanies it. In automated instrumented UAP detection, recording a sighting as occurring at 21:14:32.417 UTC creates an impression of precision, but that number alone does not reveal whether the true observation time is known within a microsecond, a video frame, a tenth of a second or several seconds. For evidence intended to support later analysis, the timestamp should therefore be treated as a measured quantity with an associated uncertainty rather than as an unquestionable fact.

Uncertainty illustration 1 This distinction becomes especially important when correlating optical recordings with radar tracks, ADS-B broadcasts, satellite ephemerides, weather observations or data from a second observing station. A timestamp that honestly reports its confidence interval is often more valuable than one that appears perfectly precise but conceals unknown delays, clock drift or software latency. This approach follows the broader principles of scientific metrology, where traceability and documented uncertainty are considered essential characteristics of reliable measurements. [NIST+2NIST]nist.govNIST Time and Frequency ServicesIf a measurement is made using a NIST reference, and if the uncertainty of the measurement is known a…

Why exact-looking times can be misleading

Digital systems routinely display timestamps with millisecond or microsecond resolution regardless of whether that resolution reflects actual measurement accuracy. A camera may save timestamps with three decimal places simply because its software uses that format, not because the exposure time is known to within one millisecond.

Several different concepts are easily confused:

Resolution is the smallest time increment the system can record.
Accuracy describes how close the recorded time is to UTC or another reference.
Precision describes repeatability.
Uncertainty expresses the estimated range within which the true event time probably lies.

A record reading 2026-04-18T21:14:32.417Z therefore communicates only a nominal time. Without additional metadata, an analyst cannot determine whether the uncertainty is ±0.5 ms, ±16.7 ms (one frame at 60 fps), ±100 ms or several seconds. ISO 8601 standardises timestamp formatting, but it does not by itself communicate measurement uncertainty or timing confidence. [ISO]iso.orgiso 8601 date and time formatISO 8601 — Date and time formatMarch 11, 2020 — 21 Feb 2017 — It gives a way of presenting dates and times that is clearly defined and…Published: March 11, 2020

For automated UAP observatories, hidden uncertainty can produce false correlations. Two sensors appearing to observe the same object may actually have recorded unrelated events if one system’s clock was offset by hundreds of milliseconds. Conversely, a genuine multi-sensor observation could be rejected because analysts mistakenly assumed the timestamps were more accurate than they really were.

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Sources of uncertainty in cameras and sensors

Timing uncertainty rarely comes from a single cause. Instead, it accumulates from several independent components throughout the acquisition chain.

Camera exposure and frame timing

Conventional video cameras observe continuously but report events as discrete frames. The timestamp may correspond to the beginning of exposure, the midpoint, the end of exposure or the time when the frame was written to memory. Rolling shutters introduce further ambiguity because different image rows are exposed at slightly different times.

Frame rate also limits temporal resolution. At 30 frames per second, consecutive frames are approximately 33.3 milliseconds apart. Unless interpolation or hardware timestamping is available, an object’s appearance may only be localised within part of that interval.

Research on machine vision consistently shows that optical measurement uncertainty depends not only on geometry but also on camera architecture, optics and acquisition parameters. [MDPI]mdpi.comThe Influence of Camera and Optical System Parameters…by J Skibicki · 2020 · Cited by 21 — The article presents the influence of t…

Clock synchronisation uncertainty

Even perfectly timestamped frames inherit uncertainty from the underlying clock.

Possible contributors include:

residual error after GPS or GNSS synchronisation;
Network Time Protocol (NTP) variation caused by changing network latency;

Precision Time Protocol (PTP) configuration and network hardware;
oscillator drift between synchronisation updates;
temporary loss of external timing references.

NIST guidance repeatedly emphasises that timestamp accuracy depends on the complete time-transfer system rather than the nominal specification of the timing protocol alone. Network conditions, clock stability and calibration all contribute to the final uncertainty budget. [NIST+2PMC]nist.govNIST Internet Time Service (ITS)The accuracy of the time stamps as seen by a user will usually be determined largely by the stability…

Uncertainty illustration 2

Processing latency

Many imaging systems timestamp events after substantial internal processing.

Potential delays include:

image sensor readout;
image compression;
USB or network transmission;
operating system scheduling;
buffering before storage;
AI inference pipelines;
database write delays.

Some of these delays are nearly constant and can be calibrated. Others vary unpredictably from frame to frame, producing timing jitter that should be reflected in the reported uncertainty.

Research on camera-IMU calibration demonstrates that timestamp offsets themselves can be estimated with uncertainty bounds rather than assumed to be exact, providing a useful model for multimodal scientific instruments. [Raphael Voges]raphael-voges.deRaphael VogesTimestamp Offset Calibration for an IMU-Camera System…July 18, 2018 — by R Voges · Cited by 29 — In order to find the off…Published: July 18, 2018

Uncertainty illustration 3

How to report timing confidence clearly

Instead of storing only a timestamp, an evidence-quality UAP event record should preserve enough metadata for another investigator to understand how trustworthy that time actually is.

A practical record might include:

FieldPurposeTimestamp (UTC)Nominal observation timeEstimated uncertaintyExample: ±8 ms (95% confidence)Time referenceGPS-disciplined, PTP, NTP, internal oscillatorSynchronisation statusLocked, degraded, holdover or unsynchronisedTimestamp originExposure start, exposure midpoint, frame end, packet arrival or file creationCalibration dateMost recent timing validationKnown systematic offsetApplied correction, if anyRemaining residual uncertaintyEstimated uncorrected timing error

This style of reporting separates the measured time from confidence in that measurement. It also enables later recalibration if improved timing models become available.

Where confidence cannot be quantified precisely, categorical labels are still preferable to silent assumptions.

For example:

Confidence labelTypical interpretationHighHardware timestamped and traceable to UTC with validated uncertaintyModerateSynchronised clock with characterised software latencyLowInternal clock with known drift or incomplete calibrationUnknownTimestamp preserved but uncertainty cannot presently be estimated

Such labels are not substitutes for numerical uncertainty where available, but they prevent analysts from treating all timestamps as equally reliable.

Preserving uncertainty strengthens evidence

A common misconception is that uncertainty weakens evidence. In scientific measurement, the opposite is usually true.

Measurements become more credible when their limitations are explicit because later analysts can incorporate those uncertainties into statistical reconstruction rather than unknowingly relying on false precision. Fields ranging from metrology to distributed sensing and medical event databases increasingly recommend preserving temporal uncertainty instead of replacing it with apparently exact timestamps. [PMC+2NIST]pmc.ncbi.nlm.nih.govTiming errors and temporal uncertainty in clinical databases…by AJ Goodwin · 2022 · Cited by 23 — In this narrative review we explo…

For automated UAP observatories, uncertainty-aware timestamps also improve reproducibility. Independent researchers can evaluate whether two events plausibly overlap in time, whether sensor disagreements fall within expected error margins, or whether an apparent discrepancy is simply explained by documented timing uncertainty.