Wide-Area Spectrum Sensing for Space Targets Based on Low-Earth Orbit Satellite Constellations: A SRFlow Model for Electromagnetic Spectrum Map Reconstruction

Fu, You; Fan, Youchen; Yi, Liu; Hou, Shunhu; Niu, Yufei; Fang, Shengliang

doi:10.3390/rs18010011

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Open AccessArticle

Wide-Area Spectrum Sensing for Space Targets Based on Low-Earth Orbit Satellite Constellations: A SRFlow Model for Electromagnetic Spectrum Map Reconstruction

by

You Fu

^†

,

Youchen Fan

^†,

Liu Yi

,

Shunhu Hou

,

Yufei Niu

and

Shengliang Fang

^*

School of Space Information, Space Engineering University, Beijing 101416, China

^*

Author to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

Remote Sens. 2026, 18(1), 11; https://doi.org/10.3390/rs18010011 (registering DOI)

Submission received: 5 November 2025 / Revised: 12 December 2025 / Accepted: 16 December 2025 / Published: 19 December 2025

(This article belongs to the Special Issue Multi-Object Detection and Feature Extraction of Remote Sensing Images)

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Abstract

To address the need for wide-area electromagnetic spectrum sensing of space targets from sparse Low-Earth Orbit constellation observations, this paper proposes SRFlow, a flow-matching generative model. We first construct a high-fidelity dataset covering diverse scenarios via STK-MATLAB co-simulation. By integrating multi-source priors and an iterative measurement injection strategy, SRFlow achieves high-quality reconstruction of full spectrum maps from sparse measurements. Experiments demonstrate that SRFlow significantly outperforms state-of-the-art baselines, including the physics-informed diffusion model RMDM, in both reconstruction accuracy (NMSE/SSIM) and computational efficiency (parameters/inference time), under both known and unknown target-position conditions. Moreover, it trains nearly an order of magnitude faster than diffusion models. This work contributes the first dedicated dataset for space-based spectrum sensing, introduces the accurate and efficient SRFlow model, and establishes a rigorous benchmark for future research.

Keywords: space targets; electromagnetic spectrum sensing; flow matching; spectrum map reconstruction; low-Earth orbit satellite constellation

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MDPI and ACS Style

Fu, Y.; Fan, Y.; Yi, L.; Hou, S.; Niu, Y.; Fang, S. Wide-Area Spectrum Sensing for Space Targets Based on Low-Earth Orbit Satellite Constellations: A SRFlow Model for Electromagnetic Spectrum Map Reconstruction. Remote Sens. 2026, 18, 11. https://doi.org/10.3390/rs18010011

AMA Style

Fu Y, Fan Y, Yi L, Hou S, Niu Y, Fang S. Wide-Area Spectrum Sensing for Space Targets Based on Low-Earth Orbit Satellite Constellations: A SRFlow Model for Electromagnetic Spectrum Map Reconstruction. Remote Sensing. 2026; 18(1):11. https://doi.org/10.3390/rs18010011

Chicago/Turabian Style

Fu, You, Youchen Fan, Liu Yi, Shunhu Hou, Yufei Niu, and Shengliang Fang. 2026. "Wide-Area Spectrum Sensing for Space Targets Based on Low-Earth Orbit Satellite Constellations: A SRFlow Model for Electromagnetic Spectrum Map Reconstruction" Remote Sensing 18, no. 1: 11. https://doi.org/10.3390/rs18010011

APA Style

Fu, Y., Fan, Y., Yi, L., Hou, S., Niu, Y., & Fang, S. (2026). Wide-Area Spectrum Sensing for Space Targets Based on Low-Earth Orbit Satellite Constellations: A SRFlow Model for Electromagnetic Spectrum Map Reconstruction. Remote Sensing, 18(1), 11. https://doi.org/10.3390/rs18010011

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Wide-Area Spectrum Sensing for Space Targets Based on Low-Earth Orbit Satellite Constellations: A SRFlow Model for Electromagnetic Spectrum Map Reconstruction

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