Synergistic Event-SVE Imaging for Quantitative Propellant Combustion Diagnostics
🤖 AI Summary
This study addresses the challenges of capturing three-dimensional particle dynamics during high-energy propellant combustion, where microsecond-scale particle motion, dense smoke obscuration, and extreme dynamic range often cause conventional imaging systems to saturate or blur. To overcome these limitations, the work introduces a novel diagnostic framework that synergistically combines a spatially varying exposure (SVE) camera with a stereo neuromorphic event camera. A smoke-likelihood-guided high dynamic range (HDR) reconstruction and event denoising mechanism is proposed, leveraging multi-cue smoke-aware fusion to generate HDR intensity maps and correct event data under heavy smoke interference. Validated on boron-based propellant experiments, the system achieves highly accurate measurements—0.56% maximum calibration error in equivalent particle diameter and separation height—and successfully captures rapid transient separation phenomena that are inaccessible to traditional imaging approaches.
Application Category
📝 Abstract
Real-time monitoring of high-energy propellant combustion is difficult. Extreme high dynamic range (HDR), microsecond-scale particle motion, and heavy smoke often occur together. These conditions drive saturation, motion blur, and unstable particle extraction in conventional imaging. We present a closed-loop Event--SVE measurement system that couples a spatially variant exposure (SVE) camera with a stereo pair of neuromorphic event cameras. The SVE branch produces HDR maps with an explicit smoke-aware fusion strategy. A multi-cue smoke-likelihood map is used to separate particle emission from smoke scattering, yielding calibrated intensity maps for downstream analysis. The resulting HDR maps also provide the absolute-intensity reference missing in event cameras. This reference is used to suppress smoke-driven event artifacts and to improve particle-state discrimination. Based on the cleaned event observations, a stereo event-based 3D pipeline estimates separation height and equivalent particle size through feature extraction and triangulation (maximum calibration error 0.56%). Experiments on boron-based propellants show multimodal equivalent-radius statistics. The system also captures fast separation transients that are difficult to observe with conventional sensors. Overall, the proposed framework provides a practical, calibration-consistent route to microsecond-resolved 3D combustion measurement under smoke-obscured HDR conditions.
Problem
Research questions and friction points this paper is trying to address.
propellant combustion
high dynamic range
smoke interference
particle motion
real-time monitoring
Innovation
Methods, ideas, or system contributions that make the work stand out.
Event-based vision
Spatially Variant Exposure (SVE)
High Dynamic Range (HDR) imaging
Smoke-aware fusion
3D particle diagnostics
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