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.

Apollo accelerator dates

Cohort 1: 26 October 2023 through 16 January 2024
Cohort 2: 01 February 2024 through 23 April 2024
Cohort 3: 01 May 2024 through 31 July 2024
Cohort 4: 06 August through 29 October 2024
Cohort 5: 05 November through 28 January 2025
Cohort 6: 05 February 2025 through 29 April 2025
Cohort 7: 14 May 2025 through 12 August 2025
Cohort 8: 27 August 2025 through 19 November 2025
Cohort 9: 10 December 2025 through 04 March 2026
Cohort 10: 25 March 2026 through 17 June 2026
Cohort 11: 08 July 2026 through 30 September 2026
Cohort 12: 21 October 2026 through 13 January 2027

Cohort 8 application window is 16 July 2025 through 06 August 2025

1. Launch Period of Interest (POI)

Automatically detect the start of a space launch cycle to define a Period of Interest (PoI). Early detection allows more time to assess potential threats and prepare appropriate responses. The capability must identify visual indicators suggestive of pre-launch activity, predict the associated launch window, and assign probabilistic confidence levels to segments of that window based on weather constraints and modeled launch opportunities (e.g., direct-ascent or coplanar trajectories).

2. Launch Commit Criteria

1. Launch Period of Interest (POI)

Automatically predict whether future weather conditions will meet space launch commit criteria. This enables timely go/no-go assessments and improves threat and hostility evaluations, especially when launch attempts deviate from expected weather tolerances. The capability must ingest global weather data, evaluate critical factors such as wind, temperature, humidity, cloud cover, precipitation, and lightning risk, and generate go/no-go predictions at 1, 3, and 6-hour intervals.

3. Direct Ascent ASAT Prediction

1. Launch Period of Interest (POI)

3. Direct Ascent ASAT Prediction

Automatically predict the time and azimuth of potential anti-satellite (ASAT) launches and assess the feasibility of engagement against the potential target(s). The capability must identify the likely targeted satellite, estimate remaining time of flight, and compute expected miss distances to support timely threat characterization. This predictive capability is essential for enabling defensive countermeasures, threat attribution, and rapid operational response to emerging space-based hostilities.

4. Coplanar Launch Prediction

5. Co-orbital Maneuver Prediction

3. Direct Ascent ASAT Prediction

Predictively identify launch windows and azimuths that enable insertion into a coplanar orbit within ±3° inclination with designated satellites. The capability must analyze inclination and RAAN alignment constraints to provide early warning of when adversaries may attempt to place vehicles in orbits capable of intercepting or shadowing friendly space assets.

5. Co-orbital Maneuver Prediction

Predictively model the sequence of orbital maneuvers required for a coplanar satellite to become co-orbital with a designated list of satellites.The capability must automatically provide early threat identification when an object maneuvers to match the orbit of high-value space assets.

6. Ascent Trajectory Prediction

5. Co-orbital Maneuver Prediction

Automatically predict the ascent trajectory of a launch vehicle from liftoff through orbital insertion. The capability should ingest launch alerts, reference vehicle parameters from a target model database, and constrain possible trajectories with historical and performance data. The capability must output trajectories in formats such as TLEs, state vectors, or ephemeris, and continuously refine predictions with updated detection inputs. This capability supports time-sensitive sensor search and reacquisition during sparse data conditions and must operate fully autonomously without user interaction.

7. Launch Nominal

8. Associate Launch Nominals to PAI

Automatically predict the initial orbital parameters (Launch Nominals) of a space launch vehicle by ingesting launch prediction or detection messages, leveraging vehicle-specific parameters from a target model database, and applying constraints based on historical launch behavior. The capability must continuously refine outputs based on updated detections, coplanar/co-orbital opportunities, and mission-specific context. Outputs should include predicted orbital elements for use in coarse sensor search operations during early post-launch phases when tracking data is limited.

8. Associate Launch Nominals to PAI

Automatically associate predicted launch orbits (Launch Nominals, represented as TLEs) with publicly available open-source information including NOTAMs, TFRs, sea lane closures, FCC filings, and press releases to refine the Launch Period of Interest (POI). The capability must ingest and geospatially process polygonal data from these sources, align them with launch vehicle ground tracks and expected stage separation/reentry zones, and estimate launch times and locations. This improves accuracy and situational awareness when official data is sparse or delayed.

9. Detect Launches

8. Associate Launch Nominals to PAI

10. Characterize Launches

Automatically detect foreign space launch events by processing data from seismic, acoustic, and kinetic sensors. The capability must identify and correlate relevant signals to determine the time and location of launch, associate multiple detections to a single event, and estimate an initial ascent trajectory.

10. Characterize Launches

11. Launch Pattern of Life (PoL)

10. Characterize Launches

Characterize detected launches as either suborbital, orbital, interplanetary.

11. Launch Pattern of Life (PoL)

Generate and maintain a launch pattern-of-life (POL) for each country, space launch facility, and space launch vehicle.

12. Fused Launch Detection

11. Launch Pattern of Life (PoL)

Automatically fuse multiple launch detection messages by associating them with a single unique launch event. The capability must iteratively refine and update launch confidence, estimated time, and location, while superseding previously published detection outputs. This enables dynamic solution improvement and ensures the most accurate, up-to-date characterization of launch activity.

13. Launch Threat Evaluation

14. Detect Objects Transiting Atmosphere

Automatically assess whether a detected space launch represents a potential threat to friendly satellites by evaluating its trajectory for characteristics of direct ascent ASAT, coplanar, or co-orbital missions. The capability must identify potential targets, calculate remaining time of flight, and determine miss distance and probability of collision. This supports real-time threat warning, space asset defense, and attribution of hostile actions.

14. Detect Objects Transiting Atmosphere

Automatically detect, localize, and track high-velocity objects transiting the upper atmosphere (30–300 km altitude) using non-traditional data sources such as ionospheric disturbances, SATCOM/GPS interference patterns, and SDR-based signal anomalies. The capability must differentiate space-capable objects such as hyperglide vehicles and ballistic missiles from conventional aircraft, associate multiple detections into a coherent track, estimate trajectory vectors, and predict impact point and timing for Earth-bound events. Outputs enable cueing and enhances situational awareness using publicly accessible or commercially available data.

15. Sensor Search

14. Detect Objects Transiting Atmosphere

16. Search to Track Transition

Optimize sensor search strategies applicable to EO, IR, RF, and Radar sensors (ground and space-based) to maximize the probability of reacquiring a target while minimizing time spent searching. The algorithm must account for the target’s last known state and uncertainty, maneuverability characteristics (e.g., thrust and acceleration), and sensor constraints such as field of regard, field of view, environmental conditions, and minimum detectable target thresholds. This capability is essential to counter adversary evasion tactics and maintain continuous custody of high-interest objects, preserving military decision-making timelines.

16. Search to Track Transition

17. Uncorrelated Track (UCT) Processing

16. Search to Track Transition

Provide a transition algorithm that enables sensors to move from search mode to track mode while maximizing the probability of correctly identifying and locking onto the intended target. The technique must distinguish between valid detections and unintended objects, reducing the risk of tracking false targets. This prevents wasted sensor resources, mitigates the loss of custody, and preserves decision-making time in time-sensitive or contested operational environments.

17. Uncorrelated Track (UCT) Processing

Provide an advanced processing technique to analyze uncorrelated tracks (UCTs) and promote candidate orbits of space objects that may be actively managing their radar or optical signatures to evade detection, tracking, and identification. The capability must distinguish between cleanup debris, large piece separations, and potential threats, and maintain a historical database of intermediate orbits (e.g., GEO transfers). It should support correlation, orbit determination, metadata labeling, and integration into test/training pipelines. Special attention should be given to shadow tracks, high angular rates, and anomalous low-altitude signatures. This capability is critical for reacquiring and maintaining custody of potentially hostile space objects in a contested space domain.

18. State Update

17. Uncorrelated Track (UCT) Processing

Automatically perform state estimation and orbit determination by associating observations, correlating tracks to existing orbits, and applying techniques such as Batched Least Squares for orbit correction. The capability must maintain an up-to-date "mega catalog" of satellite states, propagate them to a common time, and perform cross-catalog correlation (e.g., ELCOMP) to identify duplicate entries, assign aliases, and update records accordingly. This persistent and accurate state maintenance is essential to enable advanced space domain awareness operations.

19. Object Classification

20. Threat Simulation Catalog

Automatically classify detected objects as either active payload, inactive payload, upper stage, or apogee kick motor.

20. Threat Simulation Catalog

Continuously update a catalog of simulated or hypothetical orbital elements (e.g., TLEs, state vectors, ephemerides) representing potential space object maneuvers, separation events, or breakups. This Threat Simulation Catalog must be accessible by sensors to assist with tactical correlation of uncorrelated tracks (UCTs), enable automated alerting when real-world data matches simulated profiles, and support UCT seeding, sensor volume tasking, and hostility assessment. The capability should enable rapid integration of simulated cases to improve detection and characterization of adversarial activity in the space domain.

21. Detect Maneuvers

20. Threat Simulation Catalog

22. Characterize Maneuvers

Automatically detect orbital maneuvers by identifying deviations from predicted state estimates or by correlating with entries in a Threat Simulation Catalog. The capability must assess the likelihood that a deviation is due to intentional maneuvering rather than environmental factors (e.g., drag or space weather), and accurately estimate the new orbital state. This enables early detection of engagement activity, providing critical time for operational response and defensive measures.

22. Characterize Maneuvers

23. Maneuver Pattern of Life (POL)

22. Characterize Maneuvers

Automatically characterize detected satellite maneuvers by classifying them into magnitude bands (e.g., small, medium, large, aggressive) and duration categories (short vs. long). This classification capability enables trend analysis, supports pattern-of-life development, and provides input for predictive modeling and space engagement simulations. Accurate characterization is essential for distinguishing routine station-keeping from potentially hostile or evasive actions.

23. Maneuver Pattern of Life (POL)

Generate and maintain maneuver pattern-of-life (POL) profiles for individual satellites by cataloging the timing, magnitude, and vector of intentional maneuvers (excluding natural orbital perturbations unless there is evidence of intentional exploitation). The capability must identify deviations from typical maneuver behavior to support early detection of potential space engagements or adversary intent shifts.

24. Detect Separation Events

23. Maneuver Pattern of Life (POL)

Automatically detect satellite separation events by identifying increases in track density within a given orbital volume. The capability must classify events based on object size, generate and track debris families, backpropagate to identify the parent satellite, and estimate the event’s time and ECI location. Accurate classification of events as sub-satellite deployments or debris-producing incidents is essential for understanding system failures, collisions, or potential hostile actions such as kinetic attacks or covert weapon deployment.

25. Characterize Separation Events

26. Separation Event Pattern of Life (POL)

Automatically classify detected debris-generating events into categories based on inferred energy levels: shedding (low-energy), explosions (medium-energy), or impacts (high-energy). This classification supports tailored reacquisition strategies and enables the identification of adversary actions, system failures, or hostile engagements, particularly for sensors tuned to detect specific threat signatures.

26. Separation Event Pattern of Life (POL)

Generate and maintain pattern-of-life (POL) profiles for satellite separation events. This historical behavioral model helps identify anomalies that may indicate covert activity or emerging threats.

27. Detect Proximity Events

26. Separation Event Pattern of Life (POL)

28. Characterize Proximity Events

Automatically predict proximity events between space objects, leveraging tools such as COMBO+ (Computation of Miss Between Orbits). The capability must calculate start/stop times, time and point of closest approach, probability of collision, and relative kinematics. Outputs may also intersect with EO/IR Weapon Engagement Zones (WEZ) data. Predicting proximity events in advance is essential for space domain awareness, anomaly detection, and the assessment of potential threats or intelligence-gathering activity.

28. Characterize Proximity Events

29. Proximity Pattern of Life (POL)

28. Characterize Proximity Events

Automatically characterize proximity events between satellites into behavioral categories such as flyby, formation flying, perch, teardrop, dynamic station keeping (DSK), walking safety ellipse, or non-maneuvering conjunctions (NMC). Accurate classification enables contextual understanding of object behavior, supports intent analysis, and informs pattern-of-life development for potential threat identification or attribution in contested space environments.

29. Proximity Pattern of Life (POL)

Generate and maintain pattern-of-life profiles for proximity events between space objects. Establishing historical behavioral baselines supports the identification of anomalous or threatening interactions and enhances predictive space domain awareness and operational readiness.

30. Detect Link Change

29. Proximity Pattern of Life (POL)

Automatically detect changes in a satellite’s RF transmissions. Identifying unexpected shifts in frequency, power, or modulation may indicate changes in mission, intent, or operational status.

31. Characterize Link Change

32. Link Pattern of Life (PoL)

Automatically characterize changes in satellite RF links by analyzing parameters including center frequency, bandwidth, power levels, signal type (e.g., beacon, telemetry), encryption status, beam pointing direction, and polarization. These characterizations provide insight into changes in operational state, payload activation, or intent, and support threat assessment, anomaly detection, and mission classification.

32. Link Pattern of Life (PoL)

Generate and maintain a pattern-of-life profile for satellite radio frequency (RF) transmissions. This includes capturing temporal, geographic, and contextual characteristics of link activity such as transmission schedules, locations, signal types, and associated mission phases. By establishing normative communication behavior, the capability can identify deviations that may indicate changes in mission, activation of payloads, or emerging threats.

33. Detect Attitude Change

32. Link Pattern of Life (PoL)

34. Characterize Attitude Change

Automatically detect changes in satellite attitude and assess its stability over time. The system must determine whether the satellite maintains a consistent orientation or exhibits signs of uncontrolled motion, tumbling, or drift.

34. Characterize Attitude Change

35. Attitude Pattern of Life (PoL)

34. Characterize Attitude Change

Characterize satellite attitude changes by distinguishing between unstable motion, deliberate reorientation, and mechanical configuration changes involving external components (e.g., antenna slewing, robotic arm activity, or cargo bay operations). This improves understanding of satellite behavior and intent.

35. Attitude Pattern of Life (PoL)

Generate and maintain pattern-of-life profiles for satellite attitude changes, encompassing both the spacecraft bus orientation and dynamic payload components such as gimbaled antennas, articulated appendages, or robotic elements. By establishing baseline behavior over time, the capability can detect deviations that may indicate mission transitions, payload activation, or emerging threats.

36. Detect Reentry

35. Attitude Pattern of Life (PoL)

Automatically detect satellite or object reentry events and accurately predict the associated impact time and location. The system must process orbital decay indicators, trajectory data, and environmental conditions to generate timely and reliable forecasts.

37. Characterize Reentry

38. Reentry Pattern of Life (POL)

Characterize predicted or detected satellite reentries as controlled or uncontrolled and assess whether they pose any hazard or risk. This supports situational awareness and informed response planning.

38. Reentry Pattern of Life (POL)

Generate and analyze reentry pattern-of-life profiles categorized by country of origin, object type or class (e.g., satellite, rocket body), and associated characteristics such as time on orbit, passivation status, compliance with disposal best practices, and hazard potential. This statistical framework supports assessment of international space behavior, identification of trends in responsible or irresponsible disposal practices, and evaluation of reentry-related risk across global actors.

39. Predict EO/IR WEZ

38. Reentry Pattern of Life (POL)

40. Predict Radio Frequency WEZ

Automatically predict valid EO and IR imaging opportunities for satellites based on orbital geometry, sensor field of view, target location, solar illumination, atmospheric conditions, and line-of-sight constraints.

40. Predict Radio Frequency WEZ

41. Predict Directed Energy Weapon WEZ

40. Predict Radio Frequency WEZ

Automatically predict valid radio frequency (RF) jamming opportunities based on satellite position, line-of-sight to potential targets, antenna pointing capabilities, power output, and the operational frequency bands of targeted systems.

41. Predict Directed Energy Weapon WEZ

Automatically predict when satellites have valid directed energy weapon (DEW) engagement opportunities, such as with lasers or high-power microwaves (HPM).

42. Predict Kinetic Kill Vehicle WEZ

41. Predict Directed Energy Weapon WEZ

Automatically predict valid kinetic kill vehicle (KKV) engagement opportunities by assessing orbital alignment, time-of-flight, closing velocity, and intercept geometry between potential threat satellites and targeted space assets. This supports early detection of potential direct-ascent or co-orbital threats to high-value space assets.

43. Predict Orbital Bombardment WEZ

Automatically predict valid orbital bombardment opportunities by assessing satellite overflight geometry, altitude, velocity, payload characteristics, and potential ground impact zones. The capability must evaluate line-of-sight, atmospheric reentry trajectories, and time-on-target constraints to identify windows where a satellite could feasibly deliver a kinetic or non-kinetic payload to Earth’s surface.

44. Predict Area Munition WEZ

43. Predict Orbital Bombardment WEZ

Automatically predict when satellites have valid opportunities to deploy area munitions, such as nuclear devices or orbital mines. This enables early warning of high-impact, wide-area threats to space or ground assets.

45. Predict Grappler or Docking WEZ

43. Predict Orbital Bombardment WEZ

45. Predict Grappler or Docking WEZ

Automatically predict valid engagement windows for grappling or docking operations between satellites, considering relative orbits, proximity thresholds, alignment geometry, approach vectors, and velocity matching. The capability must account for cooperative and non-cooperative scenarios, including relative motion constraints, mechanical interface requirements, and dwell time.

46. Sensor Orchestration

47. Camouflage, Concealment, Deception, Maneuver (CCDM) Evaluation

45. Predict Grappler or Docking WEZ

Generate an advanced sensor orchestration system capable of dynamically modeling sensor capabilities including field of view, field of regard, slew/settle rates, exclusion zones, and detection thresholds and optimizing tasking schedules across a heterogeneous sensor network. The system must resolve conflicting cues based on data provenance and cue quality, maintain red/blue object lists and risk maps (e.g., weapon engagement zone thresholds), and calculate custody status and positive identification/confident identification (PID/CID) in real time. Custody metrics should incorporate sensor integration time, MDT thresholds, gap times, and delta-V estimates. The orchestration system must support multiple operational modes, including surveillance, custody, characterization, and threat warning & assessment, to maintain strategic situational awareness and support timely decision-making in contested space environments.

47. Camouflage, Concealment, Deception, Maneuver (CCDM) Evaluation

Generate analytical techniques and automated tools to evaluate space objects for indicators of camouflage, concealment, deception, or maneuver (CCDM). The capability must assess behavioral, kinematic, electromagnetic, and regulatory anomalies including deviations in stability, maneuver and RF pattern-of-life, sub-satellite deployment, registry violations, unexpected signatures, proximity activity, launch manifest discrepancies, and association with known threat vectors. These indicators, derived from historical threat intelligence, must be synthesized into a triage framework that alerts operators to objects potentially acting as space-based weapons or force enhancement capabilities. Integration with UCT detection, especially in GEO, is required to support real-time threat characterization and battle manager decision support.

48. Dynamic Red OOB

47. Camouflage, Concealment, Deception, Maneuver (CCDM) Evaluation

Generate an automated process to manage a Red List of space objects seamlessly adding, removing and updating the rank of objects as appropriate based on the evaluation of camouflage, concealment, deception, and maneuver (CCDM) indicators. The capability must determine when a minimum set of indicators signals a credible threat, identify which indicators are incomplete, map those gaps to appropriate sensor capabilities, and dynamically task sensors to collect the necessary data. This enables continuous refinement of the Red List to support timely threat detection.

49. Satellite Bus Modeling

50. Satellite Payload Modeling

Generate techniques to model fundamental satellite bus characteristics such as propulsion capabilities (thrust, max delta-V), power system attributes (e.g., max transmit power), physical dimensions, area-to-mass ratio (AMR), and total mass by processing open-source information, observed behaviors, and advanced methods like inverse light curve analysis or resolved imaging. These derived parameters support the identification of anomalous or threatening objects (e.g., weapons systems, nuclear materials, rapid mass loss) and inform weapon engagement zones.

50. Satellite Payload Modeling

Generate techniques to model satellite payload characteristics such as sensor or weapon type (e.g., EO, IR, RF, radar, DE, munition), aperture or dish size, sensitive wavelengths, and maximum delta-V for munitions using open-source information, observed satellite behavior, and novel analysis methods.

51. Target Model Database

50. Satellite Payload Modeling

52. Hostile Intent Assessment

Generate a dynamically updated Target Model Database (TMDB) that serves as a centralized digital repository of current satellite bus and payload models. The TMDB must support real-time queries by downstream services, handle information requests when data is outdated, incomplete, or low confidence, and allow third-party analytic providers to both retrieve and contribute data. The capability must include logic to manage and reconcile conflicting inputs, merging competing results from multiple providers to maintain data integrity and ensure the highest-confidence characterization of orbital targets.

52. Hostile Intent Assessment

Automatically assess the intent of observed satellite behaviors and space events, classifying them as hostile, potentially hostile, or non-hostile. The capability must evaluate factors such as the imminent use of force, maneuvers that enable weapon engagement opportunities, pursuit behaviors, violations of established patterns of life, and other contextual information.

53. Response Recommendation

52. Hostile Intent Assessment

53. Response Recommendation

Provide an automated decision support capability that generates dynamic defensive response recommendations based on predicted weapon engagement opportunities, hostile intent assessments, conflict stage, Standing Rules of Engagement (SROEs), commander’s intent, and evaluated risks or vulnerabilities. The capability must structure recommended courses of action into a hierarchy of mission objectives (e.g. Do Nothing, Monitor, Mitigate, Evade, Attribute, or Assess) and continuously refine or supersede these recommendations as new data becomes available.

54. Combat Assessments

52. Hostile Intent Assessment

53. Response Recommendation

Perform the following assessments: hostility, strike, munitions effectiveness, battle damage, collateral damage. Strike assessment (often termed post-strike assessment) refers to the immediate evaluation of a strike’s outcome, usually conducted by operations personnel in near real-time. Its purpose is to quickly measure whether the strike achieved its intended performance against the target, providing an initial sense of success or failure. Battle damage assessment (BDA) is to provide commanders with timely, accurate estimates of what happened to the target – essentially, did the strike achieve the desired effect on the target’s capabilities. Collateral damage assessment (CDA) addresses the unintended or incidental effects of military strikes, particularly harm to civilians and damage to civilian objects or other non-combatant entities. Munitions Effectiveness Assessment (MEA) is the component of combat assessment that evaluates how well the weapons or munitions employed performed and whether they functioned as expected.

55. Atmospheric Modeling

Develop an atmospheric model which improves on the current state of the art in terms of temporal and spatial resolution and density estimation accuracy. Understanding how drag affects satellites is important for improving propagators and associating weather events with spacecraft events.

56. Space Weather

55. Atmospheric Modeling

Develop a dynamic space weather model that includes Solar Radiation Pressure (SRP). Understanding how SRP and other space weather phenomena affect satellites is important for improving propagators and associating weather events with spacecraft events.

By viewing, accessing, and/or downloading information from sdataplab.org, you agree to indemnify, defend, and hold harmless the US Government and the SDA TAP Lab from any and all liabilities, claims, demands, damages, losses and expenses (including, without limitation, defense costs and reasonable attorney fees) arising out of, or resulting from, the lack of accuracy or correctness of the data, or the use of the data. You may notify the TAP Lab of any errors or discrepancies found in using the data through our Contact Us link

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

1. Launch Period of Interest (POI)

1. Launch Period of Interest (POI)

1. Launch Period of Interest (POI)

2. Launch Commit Criteria

1. Launch Period of Interest (POI)

1. Launch Period of Interest (POI)

3. Direct Ascent ASAT Prediction

1. Launch Period of Interest (POI)

3. Direct Ascent ASAT Prediction

4. Coplanar Launch Prediction

5. Co-orbital Maneuver Prediction

3. Direct Ascent ASAT Prediction

5. Co-orbital Maneuver Prediction

5. Co-orbital Maneuver Prediction

5. Co-orbital Maneuver Prediction

6. Ascent Trajectory Prediction

5. Co-orbital Maneuver Prediction

5. Co-orbital Maneuver Prediction

7. Launch Nominal

8. Associate Launch Nominals to PAI

8. Associate Launch Nominals to PAI

8. Associate Launch Nominals to PAI

8. Associate Launch Nominals to PAI

8. Associate Launch Nominals to PAI

9. Detect Launches

8. Associate Launch Nominals to PAI

10. Characterize Launches

10. Characterize Launches

11. Launch Pattern of Life (PoL)

10. Characterize Launches

11. Launch Pattern of Life (PoL)

11. Launch Pattern of Life (PoL)

11. Launch Pattern of Life (PoL)

12. Fused Launch Detection

11. Launch Pattern of Life (PoL)

11. Launch Pattern of Life (PoL)

13. Launch Threat Evaluation

14. Detect Objects Transiting Atmosphere

14. Detect Objects Transiting Atmosphere

14. Detect Objects Transiting Atmosphere

14. Detect Objects Transiting Atmosphere

14. Detect Objects Transiting Atmosphere

15. Sensor Search

14. Detect Objects Transiting Atmosphere

16. Search to Track Transition

16. Search to Track Transition

17. Uncorrelated Track (UCT) Processing

16. Search to Track Transition

17. Uncorrelated Track (UCT) Processing

17. Uncorrelated Track (UCT) Processing

17. Uncorrelated Track (UCT) Processing

18. State Update

17. Uncorrelated Track (UCT) Processing

17. Uncorrelated Track (UCT) Processing

19. Object Classification

20. Threat Simulation Catalog

20. Threat Simulation Catalog

20. Threat Simulation Catalog

20. Threat Simulation Catalog

20. Threat Simulation Catalog

21. Detect Maneuvers

20. Threat Simulation Catalog

22. Characterize Maneuvers