BMC3I TAP Lab Cohort 2

Summer 2025Colorado Springs, CO
Customer: State of Colorado (OEDIT Grant)
Problem Sponsor: BMC3I TAP Lab

The BMC3I TAP Lab accelerates the delivery of space battle management solutions to defense centers by connecting commercial software companies with the testing, compliance support, and environment integration they need to succeed.

Through a structured cohort program, beginning with an Ask Me Anything webinar and Catalyst Wine Mixer and progressing into a six-week Stage 1 cohort experience, selected companies gain direct access to government and industry subject matter experts, customer discovery opportunities, and a growing network of partners working to strengthen the defense industrial base.

The result is a faster, lower-risk path for small businesses to deliver real capability to the warfighter.

Problem Statement

Cohort 2 ran June 9–August 1, 2025, hosting three companies working across the following problem sets:

SDA TAP Lab Problem 1: Using commercial or public imagery, detect the start of a space launch cycle automatically.

SDA TAP Lab Problem 3: Using seismic data, commercially available cell-phone accelerometer data, or weather data, detect the time and location of foreign space launches automatically.

SDA TAP Lab Problem 7: Using orbital data and/or knowledge of sensors and satellites, develop a sensor search technique that maximizes the probability of reacquiring a satellite or space launch vehicle. The technique must be valid for ground or space based EO, IR, RF, or RADAR sensors.

SDA TAP Lab Problem 9: 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 identification.

SDA TAP Lab Problem 11: Using orbital data, automatically detect separation events and classify them as either 1) sub-satellite deployment, 2) Debris generating event.  Further sub-classify debris generating events, as either: Shedding, Explosion, Impact

SDA TAP Lab Problem 16: 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 unknown (UNK), debris (DEB), rocket body (R/B), or an inactive payload:

    • Object is stable
    • Stability has changed

SDA TAP Lab Problem 20: 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.

    • 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
    • Add shape stuff
    • 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
    • Other

Schedule

July 8-11, 2025Kickoff & Virtual Programming
July 15-25, 2025In-Person Programming
July 29-August 22, 2025Virtual Programming

Cohort Companies

At BQP, we’re developing a quantum-accelerated digital twin platform for mission-critical applications. The platform, BQPhy, is a back-end engine that integrates seamlessly into existing engineering workflows and operates on today’s infrastructure without requiring quantum hardware. We are bridging the gap between current high-performance computers and future quantum computers with probabilistic algorithms from quantum information science, unlocking simulation capabilities that were previously impractical or impossible. BQPhy reduces time, accuracy, and throughput inefficiencies, thereby accelerating development cycles, unlocking previously unexplored design spaces, lowering costs, and enhancing product outcomes.

Paterson Aerospace Systems Corp.

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Paterson Aerospace Systems Corp. is taking a sustainable approach to space exploration through research and development. Our current products aim at improving Space Domain Awareness, by providing automated tracking, detection and maneuvering of objects in earth orbit. For this reason, we are developing technologies capable of tracking the millions of objects smaller than 10cm, which are orbiting the earth and threatening key systems. By giving secure access to this data, as well as automated analysis and trajectory mapping, this technology has the potential to prevent a catastrophic future where access to space is virtually impossible. As humanity moves toward its interplanetary destiny, we will need to be more conscious of our environmental impact on Earth and beyond. At Paterson Aerospace Systems, it is our duty to develop technologies to secure humanity's safe and sustainable future among the stars.

R4C Tech specializes in developing advanced cyber-physical intelligence software systems that detect and characterize threats, extract advanced insights, and enable data-driven decision-making. R4C Tech is led by experienced scientists and innovators in aerospace, defense, and artificial intelligence, dedicated to pioneering intelligent systems that significantly enhance national security and critical infrastructure resilience. We are building a suite of tools to support the integration and automation of space domain awareness technologies including a machine learning- and weather-based predictor for a future foreign rocket launch probability given several probable launch windows.

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