Technological Evolution of Ionospheric Radars and Perspectives Toward Intelligent Space Situational Awareness Systems

This study investigates the technological evolution of ionospheric radars toward the establishment of intelligent Space Situational Awareness systems, offering key strategic perspectives. The limitations of 1D structural analysis and resolution degradation inherent in conventional single-beam and Doppler beam swinging (DBS) configurations have been effectively mitigated by integrating spatial interferometry, range imaging (RIM), and aperture synthesis imaging to achieve precise 3D observations. Furthermore, the convergence of software-defined radio (SDR) platforms with multiple-input multiple-output (MIMO) technologies has circumvented traditional hardware rigidities. This synergy facilitates interference-controlled, high-density multi-static networks, elevating the radar's operational scope into critical Space Situational Awareness infrastructure. Leveraging these architectural innovations, advanced systems now combine 3D volumetric imaging with event-driven tracking algorithms, ensuring uninterrupted ionospheric monitoring while enabling real-time, precision tracking of micro-scale space debris. Moreover, coupling the exponentially growing multidimensional observational data with advanced deep learning models enables the morphological post-classification of local equatorial plasma bubbles (EPBs) with an accuracy of over 93% using only single-radar images, substantially resolving data processing latency and driving the evolution toward automated, intelligent real-time Space Situational Awareness monitoring systems. Ultimately, by systematically reviewing the capability enhancements derived from radar-AI fusion, this paper delineates future challenges regarding real-time mass data processing and the expansion of global observational coverage, thereby proposing technological and academic directions for a preemptive space security infrastructure.