Bio-Inspired Battlefield Environmental Situation Awareness

In Phase II of BIBESA, Aret investigated and developed a bio-directed algorithmic approach for optimizing situational awareness methods tied to semi-autonomous environmental surveillance. Arete proposes to expand these capabilities by leveraging a sensor network working to provide enhanced early warning for both semi and fully autonomous operation. Applicable elements of the navigational and planning tools will be applied across sensor assets, while employing orthogonal sensing modalities for cueing. This will be demonstrated via fusion of simulations using synthetic injections with live field tests data; both based on chem-bio sensing modalities (i.e. aerosol particulate monitoring, LIDAR, FP monitors). Key developments will be mapping of fixed station network sensing analytics to a moving sensing platform (i.e. UAV), and enhanced implementation of multi-modal data inputs resulting in actionable outputs. The outputs shall be quantified to allow for multiple course of action decisions based on scenario context and mission goals. Both a-priori (directed) and anomaly based detections will be investigated. Aret will deliver a beta version of the analytics, demonstrate them on a GPR based visualization platform, and produce ICDs for integration with decision engine systems. Elements of the GUI will be augmented to allow the end user to provide feedback for further optimization.