CoDAS: Cognitive Distributed Analytics in Space

Existing satellite-based systems leverage infrared technology to track missiles during the boost phase by detecting heat generated by the missile engine. Limitations in rapid geographic positioning combined with a very short boost-phase duration makes intercepts very challenging. During the critical post-boost and subsequent midcourse phases, when the missile becomes much colder, missile tracking signatures can be lost. A Space Sensor Layer comprised of Low Earth Orbit (LEO) satellites equipped with radar or optical-based sensors could detect, track, and distinguish warheads from decoys and debris during midcourse and could complement an infrared-based system. Compatibility issues and information exchange delays, however, could affect performance since LEO satellites are limited in size weight and power. Machine Learning approaches for analytics extraction provide significant performance and power saving advantages over conventional methods. Lucid Circuit, a Los Angeles-based startup, is developing an adaptable Artificial Intelligence microchip called AstrumTM for cognitive aerospace applications. By enabling machine learning in LEO space platforms, many risks and challenges are mitigated. Only the resulting critical analytics are transmitted - making them available to other satellites and strategists on the ground in real-time. LEO satellites will be able to perform distributed cognitive analytics while ensuring intelligent data attestation.