Multi-spectral and multi-platform approaches to space object monitoring: A survey

The exponential growth of orbital infrastructure and space debris accumulation presents unprecedented surveillance challenges requiring sophisticated multi-spectral monitoring systems and active removal technologies to ensure sustainable space operations. Current limitations include fragmented tracking capabilities across different size regimes, atmospheric interference restrictions, limited international data sharing protocols, and insufficient real-time processing for debris populations exceeding 47,000 tracked objects. This review comprehensively examines detection methodologies employing ground-based radar and optical systems, space-based platforms, passive RF techniques, machine learning frameworks achieving exceptional accuracy, and distributed CubeSat constellations, alongside active mitigation strategies including tethered debris systems, net-based capture mechanisms, and multi-tether configurations for debris tracking. The key contributions include an approach to assessing the most relevant research, systematic classification of multi-modal sensing architectures, critical analysis of machine learning applications that demonstrate substantial processing enhancements, evaluation of hybrid terrestrial-orbital networks integrated with deployment strategies for debris capture, and identification of future research directions toward autonomous collision avoidance and sustainable traffic management systems.