India Takes a Giant Leap in Space Surveillance with AI-Powered MOSAIC Architecture

India's space situational awareness ecosystem has received a significant boost with the unveiling of Digantara's MOSAIC AI architecture. This innovative system is designed to detect, search for, and track large numbers of objects in orbit simultaneously, providing actionable orbital intelligence to users. With its emphasis on artificial intelligence, MOSAIC is poised to address one of the central challenges facing satellite operators and military space organizations: maintaining an accurate picture of the increasingly crowded orbital environment.

The ability to track hundreds of objects simultaneously is becoming increasingly important as the number of satellites, debris objects, and other resident space objects continues to grow. Traditional space surveillance architectures can struggle to maintain persistent custody of large numbers of objects due to the distributed nature of observations and the substantial processing and correlation requirements. MOSAIC's AI-driven approach is designed to automate much of the detection, correlation, and tracking process, combining observations and identifying patterns across multiple objects and time periods.

Digantara's existing technology stack provides a foundation for MOSAIC's wide-area approach. The company's PRISM family provides space-based electro-optical sensing, while its LOCUS platform focuses on high-precision resident space-object tracking and orbit propagation. Digantara's systems are designed to detect, catalogue, and characterise objects while generating orbital information for operational users. The AIRA architecture, which integrates data from space-based and ground-based sensors, uses the proprietary AI/ML processing system, Or-Eng, to transform raw orbital observations into intelligence.

MOSAIC's wide-area approach could provide the sensing and processing layer required to rapidly build an orbital picture before individual objects are subjected to more detailed analysis. This is particularly important for identifying previously uncorrelated objects, monitoring satellite behaviour, and detecting changes in orbital patterns. The architecture also has potential military applications, as space domain awareness is increasingly becoming an essential component of national security. Knowing where potentially hostile or unidentified objects are located, what they are doing, and how their behaviour changes can provide an important strategic advantage.

Digantara's existing systems already identify applications including resident space-object detection and tracking, missile detection, orbital ISR, object characterisation, and behavioural profiling. The autonomous element of MOSAIC is equally significant, as a modern space-surveillance network cannot depend entirely on human operators manually examining every observation. AI-based processing can potentially prioritise suspicious objects, correlate observations from different sensors, and generate alerts when orbital behaviour deviates from expectations.

This could eventually allow operators to move from simply maintaining an orbital catalogue to conducting continuous behavioural monitoring of objects in space. For India, the development is particularly relevant as the country seeks to build greater sovereign Space Domain Awareness capabilities. A domestically developed system capable of combining space-based and ground-based observations could reduce dependence on foreign orbital data while providing Indian military and civilian users with a common operational picture.

Digantara has already developed an end-to-end approach spanning sensors, data processing, and analytics. Its AIRA architecture is designed to fuse diverse datasets, while its MAP platform provides mission planning, collision avoidance, satellite health monitoring, and other operational functions. MOSAIC could therefore represent another step toward creating an integrated Indian orbital-intelligence architecture rather than simply another standalone space-surveillance sensor.

The significance of the system will ultimately depend on its demonstrated detection range, object-size sensitivity, revisit rate, false-alarm performance, and ability to maintain custody of objects under challenging conditions. Nevertheless, the underlying concept reflects the direction in which space surveillance is moving: from isolated observations toward persistent, automated, and AI-driven orbital intelligence. As the number of objects in orbit continues to grow, India's ability to track and understand the orbital environment will be crucial to its national security and economic interests.