A Spatio-Temporal Memory System for Multi-Modal Sensor Fusion

Abstract:
Testing and assessment of complex systems requires the ingest, correlation, and analysis of a variety of heterogeneous data. This is particularly true in the testing, analysis, and reporting of multi-sensor data. Complex sensor systems available on Unmanned Air System (UAS) platforms require advanced techniques to enhance their resiliency and survivability. This includes autonomous, artificial intelligence (AI) architectures that can process, analyze, and provide actionable intelligence in terms of understanding and reporting on actions and events focused on system failures, either through degradation over time or operational mistakes. Unmanned vehicles must operate in unstructured environments that are inherently unpredictable and dynamical. An autonomous UAS must have some degree of cognitive intelligence [1] to undertake tasks without direct and continuous human involvement, especially in unknown environments. This includes storing, correlating, and retrieving spatial and temporal tags and information associated with sensor readings. Here we present the initial design and testing of a Spatio-Temporal database system capable of storing and correlating complex spatio-temporal information that allows inferences to be made across both time and space (geography) to provide situational awareness to sensor processing analysts. An example is provided demonstrating the power and effectiveness of the Spatio-Temporal Database Memory (STDM) and associated correlation algorithms.

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