QIS Sense & Control

Design, development, characterization, and integration of technologies related to positioning, localization, and timing, with particular emphasis on fundamental techniques of information fusion from heterogeneous sensing sources. Specific efforts involve development of robust mGNSS, low size, weight and power integration of fused sensing solutions, and opportunistic aiding of localization estimates through non-traditional modalities such as vision.

  • PNT Multi-Sensor Fusion and Modeling: This effort encompasses the development of theory and algorithms comprising open source, academic, and custom ARL software for sensor fusion, with particular focus on positioning and localization applications. The effort particularly focuses on development and modeling of tightly and loosely coupled methods for fusing EO/IR vision, inertial data, M-GNSS with anti-jam capabilities, higher-level semantic information, and other alternative aiding sensors.
  • PNT Opportunistic Error Reduction and Aggregation of Signals of Opportunity: This effort encompasses the development of theory and algorithms to leverage opportunistic reduction of error in a fused sensor localization estimate through exploitation of signals of opportunity. The effort includes incorporation of M-GNSS with anti-jam capabilities, celestial sensing, and visual methods leveraging deep learning to aid in the reduction of error in a position estimate.
  • PNT Testbed and Clock Characterization: This effort leverages the fusion and VIO efforts and involves the development, implementation, and characterization of a testbed comprising open source, academic, and custom ARL hardware and software for size, weight and power constrained sensor fusion. The PNT Testbed will allow a variety of internally and externally developed techniques to plug seamlessly into a central architecture for experimentation, flight testing, and validation and verification. Furthermore, this effort includes clock and synchronization characterization between embedded systems.

 

Principal Investigator(s): 
Shuowen Hu
Suya You