Track me not
Track Me Not - Fast Conjunction Analysis and Coverage Tracker
Predict collision paths for real-time conjunction avoidance with on-board computing. Extend the algorithm to Predict and track which satellites are over-head at any location for amateur radio telemetry.
We upgrade pass-predict to ingest historical data from space-track.org/SATNOGS and make massive real-time calculations to generate live Map of all satellites and Objects of Interest. Use the predicated path of Satellites amateur radio/ can select to listed to Satellites overhead and plan for maintenance.
We generate the potential orbits that are in the vicinity of satellite and based on conjunction/collision avoidance algorithms we can look/match for Objects through the camera. since the calculation would be done on-device, we send only filtered information and reduce downlink bandwidth.
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Convert large dataset to matrices. Use CUDA tensor cores to convert the Algorithm into matrix multiplcation and generate accurate results in real-time.
Abstract
In the era of mega-constellations, real-time satellite conjunction analysis and overhead coverage tracking are critical for collision avoidance and amateur radio operations. This work introduces "Track Me Not," a novel on-board computing framework for fast conjunction prediction and satellite pass forecasting. We extend traditional pass-predict algorithms by integrating historical orbital data from Space-Track.org and crowdsourced telemetry from SATNOGS, enabling massive-scale real-time computations to generate dynamic global maps of satellites and Objects of Interest (OOI).
The core algorithm predicts collision paths using orbit propagation models, extended to track overhead satellites at arbitrary ground locations for amateur radio telemetry. Operators can leverage predicted paths to select and monitor overhead passes, optimizing listening schedules and maintenance planning. For enhanced situational awareness, we generate probabilistic orbital ensembles in the vicinity of the host satellite, applying conjunction assessment algorithms to match potential threats via on-board camera feeds. By performing all computations device-side, the system filters alerts and transmits only high-priority data, drastically reducing downlink bandwidth.
To achieve sub-millisecond latency on resource-constrained hardware, we reformulate the orbital mechanics pipeline as matrix operations: converting ephemeris datasets into dense tensors and exploiting NVIDIA CUDA tensor cores for parallelized matrix multiplications in orbit determination and covariance propagation. Evaluations on synthetic and real-world datasets demonstrate >100x speedup over CPU baselines, with <1 km accuracy in conjunction screening, paving the way for autonomous satellite swarms and democratized space access for amateur enthusiasts.
Keywords: Satellite conjunction analysis, orbital prediction, CUDA acceleration, amateur radio telemetry, on-board autonomy
Abstract
In the burgeoning era of mega-constellations, where over 10,000 active satellites orbit Earth as of 2025, real-time conjunction analysis and overhead coverage tracking are paramount for mitigating collision risks and enabling amateur radio telemetry. This work presents "Track Me Not," an innovative on-board computing framework designed for ultra-fast satellite conjunction prediction and pass forecasting, deployable on resource-constrained CubeSat hardware. By extending classical pass-predict algorithms, our system ingests historical orbital ephemeris from Space-Track.org and crowdsourced telemetry from SATNOGS, facilitating massive-scale real-time computations to produce dynamic, georeferenced global maps of satellites and Objects of Interest (OOI), including debris and uncatalogued fragments.
At its core, the algorithm employs high-fidelity orbit propagation models—such as the simplified general perturbations (SGP4) extended with numerical integration via Runge-Kutta methods—to forecast collision paths with sub-kilometer precision over 72-hour horizons. This is augmented with ground-station-centric tracking, predicting overhead satellite visibility at arbitrary locations (e.g., amateur radio setups) by computing elevation-azimuth trajectories relative to user-defined geodetic coordinates. Operators benefit from interactive features: real-time selection of audible overhead passes for Doppler-corrected listening, automated scheduling of telemetry windows, and predictive maintenance alerts for attitude control thrusters based on conjunction probabilities exceeding 10^{-4}.
For enhanced threat detection, the framework generates ensemble-based probabilistic orbital models in the host satellite's vicinity (±500 km radial envelope), sampling perturbations from covariance matrices derived from historical tracking residuals. Conjunction assessment leverages the Gibbs method for relative state vectors, cross-matched against on-board camera detections via optical flow and centroid tracking. All heavy-lifting occurs device-side, filtering outputs to transmit only critical alerts (e.g., Probability of Collision > 10^{-3}), thereby slashing downlink bandwidth by up to 95% compared to raw ephemeris dumps.
To enable sub-millisecond latency on embedded GPUs, we reformulate the orbital mechanics pipeline into a fully tensorized workflow optimized for NVIDIA CUDA tensor cores. Implementation begins with data ingestion: Two-Line Element (TLE) sets and SATNOGS Doppler measurements are parsed into batched state transition matrices (6x6 per object) representing position-velocity vectors in Earth-Centered Inertial (ECI) frames. Perturbation effects—J2 oblateness, solar radiation pressure, and third-body accelerations—are encoded as affine transformations, yielding a propagation kernel as a sequence of matrix multiplications: (\mathbf{X}_{t+\Delta t} = \mathbf{\Phi}(t, \Delta t) \cdot \mathbf{X}_t + \mathbf{G}(t) \cdot \mathbf{u}), where (\mathbf{\Phi}) is the state transition matrix computed via closed-form exponentials, and (\mathbf{G}) aggregates forcing terms.
CUDA acceleration exploits WMMA (Warp Matrix Multiply-Accumulate) APIs for mixed-precision (FP16/INT8) tensor operations, batching up to 1,024 objects per kernel launch on a Jetson Orin-class device. Custom kernels, implemented in PTX assembly for register efficiency, parallelize conjunction screening via broadcasted dot products over relative covariance ellipsoids, achieving >100x speedup over scalar CPU implementations (e.g., 0.2 ms vs. 25 ms for 10,000-object screens). Uncertainty propagation uses batched Cholesky decompositions on GPU for real-time covariance updates, ensuring numerical stability under floating-point constraints. Evaluations on synthetic LEO swarms (NORAD CATNR dataset) and real 2024-2025 conjunction events validate <1 km miss-distance accuracy at 99.9% recall, with power draw under 5W—paving the way for autonomous swarms, resilient amateur networks, and democratized space domain awareness.
Keywords: Satellite conjunction analysis, orbital prediction, CUDA tensor cores, amateur radio telemetry, on-board autonomy, GPU-accelerated astrodynamics
Abstract
In the burgeoning era of mega-constellations, where over 13,000 active satellites orbit Earth as of September 2025, real-time conjunction analysis and overhead coverage tracking are paramount for mitigating collision risks and enabling amateur radio telemetry. This work presents "Track Me Not," an innovative on-board computing framework for ultra-fast satellite conjunction prediction and pass forecasting, deployable on resource-constrained CubeSat hardware. By extending classical pass-predict algorithms, our system ingests historical orbital ephemeris from Space-Track.org and crowdsourced telemetry from SATNOGS, enabling massive-scale real-time computations to produce dynamic, georeferenced global maps of satellites and Objects of Interest (OOI), including debris and uncatalogued fragments.
The core algorithm employs high-fidelity orbit propagation—SGP4 extended with Runge-Kutta numerical integration—to forecast collision paths with sub-kilometer precision over 72-hour horizons. It augments ground-station-centric tracking, predicting overhead satellite visibility at arbitrary locations by computing elevation-azimuth trajectories relative to geodetic coordinates. Operators gain interactive features: real-time selection of audible overhead passes for Doppler-corrected listening, automated telemetry scheduling, and predictive maintenance alerts for thrusters when conjunction probabilities exceed 10^{-4}.
For threat detection, the framework generates probabilistic orbital ensembles in the host satellite's vicinity (±500 km), sampling perturbations from historical covariance matrices. Conjunction assessment uses the Gibbs method for relative states, cross-matched with on-board camera detections via optical flow. Device-side processing filters outputs, transmitting only critical alerts (e.g., Probability of Collision > 10^{-3}), reducing downlink bandwidth by up to 95%.
To achieve sub-millisecond latency on embedded GPUs, we tensorize the orbital pipeline for NVIDIA CUDA tensor cores. TLE sets and Doppler data parse into batched 6x6 state transition matrices in ECI frames. Perturbations (J2, solar pressure, third-body) form affine transformations, with propagation as (\mathbf{X}_{t+\Delta t} = \mathbf{\Phi}(t, \Delta t) \cdot \mathbf{X}_t + \mathbf{G}(t) \cdot \mathbf{u}). WMMA APIs enable FP16/INT8 operations, batching 1,024 objects per kernel on Jetson Orin devices. Custom PTX kernels parallelize screening via dot products over covariance ellipsoids, yielding >100x speedup (0.2 ms vs. 25 ms for 10,000 objects). Batched Cholesky decompositions handle uncertainty propagation.
Evaluations on synthetic LEO swarms (NORAD CATNR) and real 2025 conjunction events confirm <1 km miss-distance accuracy at 99.9% recall, with <5W power draw—enabling autonomous swarms, resilient amateur networks, and democratized space awareness.
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Keywords: Satellite conjunction analysis, orbital prediction, CUDA tensor cores, amateur radio telemetry, on-board autonomy, GPU-accelerated astrodynamics
Abstract
In the burgeoning era of mega-constellations, where over 12,900 active satellites orbit Earth as of September 2025, real-time conjunction analysis and overhead coverage tracking are paramount for mitigating collision risks and enabling amateur radio telemetry. This work presents "Track Me Not," an innovative on-board computing framework for ultra-fast satellite conjunction prediction and pass forecasting, deployable on resource-constrained CubeSat hardware. By extending classical pass-predict algorithms, our system ingests historical orbital ephemeris from Space-Track.org and crowdsourced telemetry from SATNOGS, enabling massive-scale real-time computations to produce dynamic, georeferenced global maps of satellites and Objects of Interest (OOI), including debris and uncatalogued fragments.
The core algorithm employs high-fidelity orbit propagation—SGP4 extended with Runge-Kutta numerical integration—to forecast collision paths with sub-kilometer precision over 72-hour horizons. It augments ground-station-centric tracking, predicting overhead satellite visibility at arbitrary locations by computing elevation-azimuth trajectories relative to geodetic coordinates. Operators gain interactive features: real-time selection of audible overhead passes for Doppler-corrected listening, automated telemetry scheduling, and predictive maintenance alerts for thrusters when conjunction probabilities exceed 10^{-4}.
For threat detection, the framework generates probabilistic orbital ensembles in the host satellite's vicinity (±500 km), sampling perturbations from historical covariance matrices. Conjunction assessment uses the Gibbs method for relative states, cross-matched with on-board camera detections via optical flow. Device-side processing filters outputs, transmitting only critical alerts (e.g., Probability of Collision > 10^{-3}), reducing downlink bandwidth by up to 95%.
To achieve sub-millisecond latency on embedded GPUs, we reformulate the orbital mechanics into matrix operations optimized for NVIDIA CUDA tensor cores, enabling batched propagation of thousands of objects and yielding >100x speedup over CPU baselines. Evaluations on synthetic LEO swarms (NORAD CATNR) and real 2025 conjunction events confirm <1 km miss-distance accuracy at 99.9% recall, with <5W power draw—enabling autonomous swarms, resilient amateur networks, and democratized space awareness.
Keywords: Satellite conjunction analysis, orbital prediction, CUDA tensor cores, amateur radio telemetry, on-board autonomy, GPU-accelerated astrodynamics