Abstract:This paper presents the Mars Dust Storm Detector (MDSD), a system that leverages the THz Opportunistic Integrated Sensing and Communications (OISAC) signals between Mars surface assets (rovers and landers) to extract environmental information, particularly dust storm properties. The MDSD system utilizes the multi-parameter sensitivity of THz signal attenuation between Martian communication devices to provide rich, real-time data on storm intensity, particle characteristics, and potentially even electrification state. This approach, incorporating HITRAN spectroscopic data and Martian-specific atmospheric parameters, allows for accurate modeling and analysis. The system's ability to repurpose THz ISAC signals for environmental sensing demonstrates an efficient use of resources in the challenging Martian environment, utilizing communication infrastructure to enhance our understanding of Mars' atmospheric dynamics. The system's performance is evaluated through extensive simulations under various Node Density Factors (NDFs), comparing different interpolation algorithms for dust storm intensity mapping. Results demonstrate that linear interpolation achieves superior accuracy (correlation >0.90) at high NDFs, while nearest-neighbor and IDW algorithms maintain complete spatial coverage in sparse networks. Error analysis identifies dust particle size uncertainty as the primary contributor to estimation errors, though the system shows resilience to Martian atmospheric variations. This work extends the opportunistic use of ISAC technology to planetary exploration, contributing to both Mars atmospheric monitoring capabilities and ISAC applications in the Internet of Space (IoS).
Abstract:The proliferation of Low Earth Orbit (LEO) satellite constellations has intensified the challenge of space debris management. This paper introduces DebriSense-THz, a novel Terahertz-Enabled Debris Sensing system for LEO satellites that leverages Integrated Sensing and Communications (ISAC) technology. We present a comprehensive THz channel model for LEO environments, incorporating debris interactions such as reflection, scattering, and diffraction. The DebriSense-THz architecture employs machine learning techniques for debris detection and classification using Channel State Information (CSI) features. Performance evaluation across different frequencies (30 GHz-5 THz), MIMO configurations, debris densities, and SNR levels demonstrates significant improvements in debris detection and classification accuracy (95-99% at 5 THz compared to 62-81% at 30 GHz). Higher SNR configurations enhance sensing performance, particularly at higher frequencies. The system shows robust performance across various debris densities and MIMO size in the THz range, with a noted trade-off between communication reliability and sensing accuracy at lower frequencies. DebriSense-THz represents a significant advance in space situational awareness, paving the way for more effective debris mitigation strategies in increasingly congested LEO environments.