Sensor fusion & radar meteorite detection
Working toward fusing radar, optical, infrasound and seismic data into one estimate of a falling body, so an event can be followed from the first telescopic detection through to the meteorite on the ground.
MetDetect: meteorites in weather radar
At NASA Johnson Space Center (ARES) I’m building MetDetect, exploiting the U.S. NEXRAD Doppler weather-radar network, a continent-scale, openly-available mesh that can detect falling meteoritic debris. Working with Paul Abell and Mark Fries (who pioneered the Doppler-radar meteorite method), the goal is to turn that network into an automatic, physically-interpretable detector of fresh meteorite falls.
The detector ingests volumetric radar scans, suppresses weather noise, clusters spatio-temporal echoes with unsupervised machine learning, and applies consistency tests (altitude–time slope, fall-consistent apparent velocities, alignment with winds, multi-scan persistence). Candidates get a confidence score, and the strongest are flagged for rapid follow-up. A planned next step is a radar-fall simulator to generate annotated synthetic data, both to train a convolutional neural network for more robust detection and to provide a forward model for parameter inference. See live results at /acm2026 and the planned in-browser viewer at /detect.
Sensor fusion & sequential estimation
A single sensor only ever sees part of an entry. The real leverage comes from fusing them: optical astrometry and photometry from camera networks, Doppler radar (low-altitude debris, drift and terminal mass), infrasound and seismic records (total energy and fragmentation altitudes), and spectra / radiometry (composition and ablation regime). My research program aims to treat each significant bolide as a multi-sensor inverse problem, combining these heterogeneous streams with sequential Bayesian estimators (Kalman-type filters) that would update the meteoroid’s state (position, velocity, mass, density, fragmentation) and the ablation/fragmentation model parameters as each observation is assimilated.
The aim is full posterior distributions for pre-atmospheric mass, bulk density, strength and fragment size-frequency, with realistic uncertainties propagated to the predicted fall ellipse, rather than single best-fit values, with a forward model coupled to hierarchical MCMC so constraints can be inferred jointly across events.
Closing the decametric gap
The 10–100 m size range is the worst-characterised in the whole small-body inventory: telescopic surveys become inefficient for objects this small and dark, while fireball networks only sample what actually hits Earth. Yet this regime dominates the impact flux responsible for Chelyabinsk-type events and probes the transition between rubble-pile asteroids and individual meteoroids, a core planetary-defense concern. Tying the multi-sensor bolide constraints to the decametric NEO population that upcoming infrared surveys will reveal (NEO Surveyor, NEOMIR) is the path to closing this gap.
From discovery to recovery
A small but growing number of asteroids have now been spotted before impact (asteroid 2023 CX1, which dropped meteorites over Normandy, among them). The endgame is full-chain, “telescope-to-ground” events: telescopic discovery and spectroscopy → predicted trajectory and impact energy → luminous flight in the camera networks → fragmentation from infrasound/seismic → dark flight in Doppler radar → prediction and recovery of the meteorites, with every step fused into one coherent, uncertainty-aware estimate. Building toward that pipeline, and helping prepare networks like FRIPON for this regime, is the goal of my sensor-fusion program.