SDA TAP LAB

About Us

Apollo Accelerator

A voluntary, collaborative, 3-month tech accelerator for industry, academia, and government to solve critical SDA challenges. Problem statements are coordinated with SDA operating community and listed below. Watch for announcements and updates.

Ops Engagement

Apollo Accelerator

The SDA TAP Lab leads Ops Engagement Forums where we review and prioritize needs to ensure kill chains close. These critical engagements align the Lab to what the space defense community needs to "fight tonight". If you are part of the Ops community, please reach out!

Kill Chains

Apollo Accelerator

Kill Chains

GEO Direct Ascent ASAT threats
GEO Co-orbital ASAT threats
LEO Direct Ascent ASAT threats
LEO Co-orbital ASAT threats
Defensive Cyber Operations

Apollo accelerator

What is the Apollo Accelerator?

The Apollo Accelerator, an initiative of the SDA TAP Lab, is a collaborative tech accelerator for industry, academia, and government to solve critical SDA challenges. Participants are given access to a sandbox with data, services, a software dev environment, and the ability to host apps. The accelerator will run in 3-month cycles, up to 4 times a year, located in Colorado Springs, Colorado.

The spirit of the Apollo Accelerator is to stimulate innovation and collaboration. Preference is given to applicants who can participate in person. However, remote participation is welcome. Participation is fully voluntary.

Through the Apollo Accelerator, we attempt to create an environment where investments can align to serve both business needs and deliver solutions for national defense. Funding is never guaranteed. We take your intellectual property seriously.

You retain IP. The SDA TAP Lab is not a party to ownership rights and intellectual property issues regarding software developed during the Apollo Accelerator unless governed by a separate contractual agreement stating otherwise. You keep your IP. We help you maximize the utility of your solution.

Problem 1

Using commercial or public imagery, detect the start of a space launch cycle automatically.

Priority: Medium

Problem 2

Problem 1

Using publicly available weather data, predict if weather conditions will satisfy space launch commit criteria automatically.

Priority: Low

Problem 3

Problem 1

Problem 3

Using seismic data, commercially available cell-phone accelerometer data, or weather data, detect the time and location of foreign space launches automatically.

Priority: Medium

Problem 4

Problem 3

Using open sources or historical data, predict launch vehicle ascent trajectories and initial orbit(s) automatically.

Priority: Medium

Problem 5

Problem 4

Problem 5

Using orbital data, evaluate whether a detected launch is an ASAT and assess the potential target(s).

Priority: Medium

Problem 6

Problem 4

Problem 5

Using scientific geophysical (ionospheric, geomagnetic, etc.), or web-based software-defined radios, detect the time, location, and vector of objects transiting through the upper atmosphere (between 30 and 300 km altitude) at "space capable" velocities automatically. If the tracked object is Earth-bound, predict the impact location and time.

Priority: High

Problem 7

Using orbital data and/or knowledge of sensors and satellites, develop a sensor search technique that maximizes the likelihood of reacquiring a satellite or space launch vehicle. The technique must be valid for ground or space based EO, IR, RF, or Radar sensors

Priority: High

Problem 8

Problem 7

Develop a method for managing multiple sensor cues that maximizes the likelihood of reacquiring a satellite or space launch vehicle.

Priority: Medium

Problem 9

Problem 7

Problem 10

Using orbital data, develop a specialized technique to process uncorrelated tracks (UCTs) and promote candidate orbits generated from UCTs which may actively manage their optical or radar signatures or otherwise be evading detection, tracking, and ID.

Priority: High

Problem 10

Using orbital data, automatically detect maneuvers of these kinds:

Small 0.1 - 1.0 m/s
Medium 1.0 - 5 m/s
Large 5 - 20 m/s
Aggressive >20 m/s
Short duration 0 - 10 minutes
Long duration >10 minutes

Priority: Low

Problem 11

Problem 10

Problem 11

Using orbital data, automatically detect separation events and classify them as either 1) sub-satellite deployment, 2) Debris generating event. Upon detection of debris generating events, classify them as either:

Shedding
Explosion
Impact

Priority: High

Problem 12

Problem 10

Problem 11

Using orbital data, automatically detect proximity events between satellite pairs.

Priority: Low

Problem 13

Using photometry or RCS taken while tracking satellites, automatically detect changes in satellite attitude. This must include the ability to determine if a satellite is stable or unstable

Priority: Medium

Problem 14

Problem 13

Using radio frequency characteristics, automatically detect changes in satellite RF transmissions such as bandwidth, channel, mode, center frequency, power, encryption, or beam pointing.

Priority: Medium

Problem 15

Problem 13

Problem 15

Using orbital data, automatically detect reentry events and predict the impact location and time.

Priority: Medium

Problem 16

Problem 15

Since we assume surprise may come through camouflage, concealment, deception or maneuver (CCDM) we must interrogate targets for evidence of CCDM. Develop techniques to evaluate whether combinations of the following are true of UCT candidate orbits, or satellites in a catalog classified as UNK, debris, rocket body, or an inactive payload:

Object is stable
Stability has changed
Maneuvers detected
Radio Frequencies (RF) detected
Sub-satellites have deployed
Maneuvers or RF pattern-of-life (POL) is out of family
Violates stated ITU or FCC filings
Class disagreement between analysts
Orbit is out of family
Optical or RADAR signature out of family
Optical and RADAR signature mismatch
Object appears to be stimulated by US, allied, or partner systems
Area-to-mass ratio (AMR) is out of family
Notable changes to AMR
Proximity events appear to be valid remote-sensing passes
Maneuvers resulted in valid remote-sensing passes ("imaging maneuvers")
Imaging maneuvers are also POL violations
Object maneuvers in sensor coverage gaps
Number of objects tracked from launch is greater than expected
Object came from launch site or vehicle known to deploy threats
UNK/DEB has semi-major axis (SMA) higher than parent satellite
True uncorrelated track (UCT) while object is in eclipse
Object is in a relatively unoccupied orbit
Object is in a relatively high radiation environment
Object is not in United Nations (UN) satellite registry

Priority: High

Problem 17

Problem 16

Problem 17

Using orbital data, generate a "mega" catalog, derived from any arbitrary number of input catalogs. Steps may include:

Forward/back propagate to a common time
Perform an ELCOMP or similar process
Identify which are the same object across catalogs
Apply an alias
Store and update

Priority: Low

Problem 18

Problem 16

Problem 17

Using orbital data, generate a maneuver pattern of life for individual satellites. This may include the timing, magnitude, and vector of maneuvers (intentional changes in kinematic tensor - not naturally occurring forces which perturb an orbit, unless there is evidence that these forces are used intentionally)

Priority: Medium

Problem 19

Using orbital data, generate an attitude change pattern of life for individual satellites. This may include both changes in bus orientation and also payload orientation (antennas that slew or gimbal, appendages that articulate, etc.)

Priority: Medium

Problem 20

Problem 19

Generate a radio frequency (RF) pattern of life for individual satellites. This may include typical bandwidth, channel, mode, center frequency, power, encryption, or beam pointing.

Priority: Medium

Problem 21

Problem 19

Problem 21

Derive sensor models dynamically from data. This may include estimation of field or regard (FOR), field of view (FOV), slew and settle rates, maximum number of "beams", solar and lunar exclusion, and other constraints. This should run automatically.

Priority: Low

Problem 22

Problem 21

Using orbital data or all source information, derive basic satellite bus specifications from open source information, satellite behaviors, or other novel methods. Examples include:

Size
Shape
Area
Area to Mass Ratio (AMR)
Mass
Propulsion (low, medium, high thrust)

Priority: Medium

Problem 23

Problem 22

Problem 23

Using orbital data or all source information, derive basic satellite payload specifications from open source information, satellite behaviors, or other novel methods. Examples include:

Type of payload (UV, EO, IR, RF, Microwave, Radar, DE, Munition)
Aperture or dish diameter
Sensitive Wavelengths (does not apply to munitions)
Max transmit power (dB), (applies to RF, microwave, radar and DE)
Max delta-V (munition)

Priority: Medium

Problem 24

Problem 22

Problem 23

Develop a process to automatically nominate objects for addition/removal to either the High Rate Revisit (HRR) list or Order Of Battle (OOB) or modify the relative rank of objects on HRR list. Consideration:

Define sets with minimum number of CCDM indicators which provide minimum confidence required to warrant concern of a threat
Dynamically evaluate which "indicator sets" are complete
Map (incomplete) indicators to sensors and modes required to get data to complete a given indicator set
Task sensors to get required data, then repeat

Priority: Medium

Problem 25

Automatically monitor, report, and correct

out-of-compliance configurations for

ground-based SDA sensor IT systems (ex. telescope observatory), networking, and related cloud-based applications.

Priority: Low

Problem 26

Problem 25

Automatically mitigate DDOS attacks on

ground-based SDA assets.

Priority: Low

Problem 27

Problem 25

Problem 27

Automatically identify and report unauthorized access to SDA systems and unauthorized alterations of SDA data traversing red and gray cyber terrain (ex. data returning from remote telescope observatories).

Priority: Low

Problem 28

Problem 27

Automatically ID and characterize increased cyber activity related to USSF Key Terrain-Cyber (KT-C) before, during, and after launches (correlate multi-domain activities).

Priority: Low

Problem 29

Problem 28

Problem 29

Provide situational awareness of cyber activities in common frameworks (i.e., MITRE ATT&CK, MITRE D3FEND, etc.)

Priority: Low

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Space Domain AWARENESS

Space Domain AWARENESS

SPace domain awareness TAP Lab Mission

About Us

Apollo Accelerator

Apollo Accelerator

Apollo Accelerator

Ops Engagement

Apollo Accelerator

Apollo Accelerator

Kill Chains

Apollo Accelerator

Kill Chains

Apollo accelerator

What is the Apollo Accelerator?

Apollo accelerator dates

apollo Accelerator problem statements

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