Conditional Generative Models for High-Resolution Range Profiles: Capturing Geometry-Driven Trends in a Large-Scale Maritime Dataset

TL;DR

This paper demonstrates that conditioning generative models on geometric factors like ship size and aspect angle enables realistic high-resolution radar signature synthesis, improving robustness across diverse maritime scenarios.

Contribution

It introduces a large-scale maritime dataset and shows that geometric conditioning effectively captures the main variability in HRRPs, advancing radar signature generation methods.

Findings

Synthesized HRRPs reproduce geometric trends observed in real data.

Conditioning on geometry improves robustness of HRRP generation.

Large-scale dataset enables comprehensive analysis of maritime radar signatures.

Abstract

High-resolution range profiles (HRRPs) enable fast onboard processing for radar automatic target recognition, but their strong sensitivity to acquisition conditions limits robustness across operational scenarios. Conditional HRRP generation can mitigate this issue, yet prior studies are constrained by small, highly specific datasets. We study HRRP synthesis on a largescale maritime database representative of coastal surveillance variability. Our analysis indicates that the fundamental scenario drivers are geometric: ship dimensions and the desired aspect angle. Conditioning on these variables, we train generative models and show that the synthesized signatures reproduce the expected line-of-sight geometric trend observed in real data. These results highlight the central role of acquisition geometry for robust HRRP generation.