Automated Air Battle Manager for Real-Time Formation Analysis
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Solution Overview
Problem
Current air battle management systems rely on human operators, which are limited by cost, accessibility, and accuracy, especially in air-gapped environments where cloud connectivity is unavailable, and struggle with real-time decision-making and formation identification.
Innovation Solution
An automated air battle manager (ABM) system that provides real-time aerial combat analysis and strategy through voice interaction, utilizing speech-to-text and text-to-speech services, context-free grammar, and formation detection algorithms to enhance situational awareness and response speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human operators are used for air battle management, then decision-making can be performed with contextual understanding, but cost increases and response speed decreases
Solution Approach 1:
The system enables self-service through automated ABM capabilities that independently perform battle management tasks without requiring human operators. The automated system processes sensor data, identifies formations, and generates commands autonomously, eliminating the need for human decision-makers in the loop while maintaining effective battle management functionality.
Solution Approach 2:
The patent replaces the mechanical human operator system with an automated computational system. Human operators are substituted with automated ABM software that uses machine learning models, sensor data processing, and algorithmic decision-making to perform battle management functions, thereby reducing cost and increasing response speed.
2Adaptability or versatility
If cloud connectivity is available, then ABM systems can access more data and resources, but accessibility in air-gapped environments is lost
Solution Approach 1:
The system segments functionality into modular components that can operate independently. The automated ABM is divided into separate modules including sensor data processing, formation identification, command generation, and communication interfaces. This modular architecture allows the system to function with limited data access in air-gapped environments while maintaining the capability to access additional resources when connectivity is available.
Solution Approach 2:
The system dynamically adapts its operational mode based on environmental conditions. When cloud connectivity is available, the ABM accesses external data resources and updates; when in air-gapped environments, it operates autonomously with locally available data. This dynamic adaptability ensures continuous functionality across different operational contexts without sacrificing data access capability when needed.
3Measurement precision
If complex formation identification algorithms are used, then detection accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing sensor data and pre-identifying potential formation patterns before final detection is required. Machine learning models are pre-trained on formation data, and the system continuously pre-processes incoming sensor streams to maintain ready-state detection capabilities. This allows complex formation identification to be performed accurately without adding significant processing delay during critical combat situations.
Data Source
AI summary
Embodiments are directed towards a computer-implemented method for communicating real-time aerial combat analysis and strategy. The method may include creating one or more communication channels for one or more affiliated pilots engaged in aerial combat to submit queries to an automated air battle manager (ABM), and for the one or more affiliated pilots to receive feedback from the automated ABM. The method may also include receiving data related to one or more enemy aircraft in sensory range of one or more friendly aircraft occupied by the one or more affiliated pilots, cross-referencing one or more captured images from the received data with one or more of global positioning system (GPS) coordinates and known flight formations, and detecting flight groups or determining geographic relationships between the one or more enemy aircraft based on the one or more cross-referenced images. The method may also include receiving one or more queries from the one or more affiliated pilots, and generating responses for the one or more queries based on real-time combat data.