An Analysis of Situational Intelligence for First Responders in Military

Abstract

Situational awareness is the sense and knowledge of one’s immediate surroundings. In safety-critical sectors, maintaining situational awareness is essential for performance and error prevention. Situational awareness (SAW) is crucial for the success of activities in many different domains, such as surveillance, humanitarian aid, and search and rescue efforts. SAW is however susceptible to enemy attacks. By giving users enhanced coverage, it increases survivability and mission capability. Recently, Smart gadgets used data to address crisis scenarios and provide real-time tracking to protect law enforcement personnel out in the field. Despite these developments, it might be challenging for first responders to get a precise feel of their surroundings due to an abundance of field data. Security teams need to be able to quickly transform this data into actionable intelligence using a few instruments at their disposal, including body cameras, fingerprint scanners, and facial recognition software. Officers can cut through the noise to acquire actual real-time situational awareness by integrating heterogeneous information into a cohesive platform. Therefore, the proposed work examines potential mitigation measures while considering hostile threats and assaults against SAW systems. Additionally, information and alarms can be instantly sent between operators and field officers using vital interface features. The optimization of the AutoML system is proposed for fusion of sensor data. AutoML classification with Bayesian and ASHA (Asynchronous successive halving algorithm) is used for situational forecasting and decision-making awareness, IoT is used to monitor data gathered from Kaggle and sensor readings, while thingspeak cloud is used to monitor sensor output.