NASA · NASA-223 · record R0165

Dynamic Targeting

National Aeronautics and Space Administration · JPL: Jet Propulsion Laboratory

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: Not reported
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Science
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

In this approach, called Dynamic Targeting (DT), traditional broad swath instruments are supplemented by more focused instruments with narrow swath and/or limited duty cycle. These instruments use tracking information from other instruments to rapidly and continuously adjust their targeting and configuration to optimize their science.

Expected benefits

We aim to develop the onboard technology necessary to allow the instruments to autonomously identify critical areas of interest (e.g. plumes, thermal anomalies) or avoidance (e.g. clouds) and retarget/reconfigure to increase science productivity while accounting for instrument operations constraints such as pointing/slewing, energy, thermal, and setup. With DT, missions could control viewing geometry to extract stereo and smart instruments could autonomously track an event during an overflight to gain a more complete picture of geophysical and other events as they evolve through time and space. DT could even be used with a single instrument to map out the extent of a plume by tracing across the outer edge of the plume distinguishing between plume and non plume signals.

System outputs

identify critical areas of interest (e.g. plumes, thermal anomalies) or avoidance (e.g. clouds)

Technology

AI classification: Agentic AISource: Agentic AI: AI systems that perform tasks or make decisions autonomously with minimal human intervention.
System name(s): Not reported
Custom-developed code: Not reported
Public source code: Not reported

Sourcing

How it was built: Not reported
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: Not reported
Privacy Impact Assessment: Not reported
Authorization to Operate: Not reported
Demographic features: Not reported
Training and evaluation data: Not reported
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported