Battlefield Threat Evaluation Using Layered Segmentation
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Solution Overview
Problem
Current decision support tools in battlefield environments lack accuracy and reliability, relying heavily on operator knowledge without effective data-driven methods to assess and mitigate threats from multiple entities.
Innovation Solution
A method that segments the battlefield into layers, collects data on entity positions and behaviors, and uses machine learning algorithms like Choquet integral and Generalized Additive Independence models to determine threat levels, enabling the generation of tactical recommendations and engagement plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If decision support tools are implemented in battlefield environments, then operator decision-making is assisted, but the accuracy and reliability of threat assessment remains insufficient
Solution Approach 1:
The battlefield environment is segmented into multiple layers based on distance ranges from the reference entity. Each layer represents a different threat zone with associated tactical actions. This segmentation allows the system to process threat data in a structured manner, improving both reliability and measurement precision by evaluating entities in context of their spatial relationship to the protected entity.
Solution Approach 2:
The system changes parameters by dynamically defining distance ranges for each layer based on entity-specific criteria rather than using fixed thresholds. This allows the threat assessment to adapt to different entity types and scenarios, enhancing the accuracy and reliability of the decision support tool by adjusting assessment parameters to match the specific battlefield context.
2Measurement precision
If multiple data parameters are collected for threat evaluation, then assessment accuracy improves, but system complexity increases
Solution Approach 1:
Data collection is organized by segmenting the battlefield into layers, with each layer associated with specific data parameters and tactical actions. This structure allows the system to collect multiple data parameters (position, behavior, dangerousness, urgency, capability) in a systematic way, improving accuracy while managing complexity through organized data categorization by spatial layer.
Solution Approach 2:
The system employs a universal data collection framework that gathers multiple types of data (position, behavior, identity, dangerousness, urgency, capability) using a single integrated approach. This multi-functional data collection system handles diverse parameters through a unified layer-based structure, reducing overall system complexity while maintaining comprehensive accuracy.
3Measurement precision
If machine learning algorithms are used to determine threat levels, then decision support accuracy improves, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-defining layers and their associated distance ranges and tactical actions before threat assessment begins. This pre-structuring of the battlefield environment allows machine learning algorithms to process threat data more efficiently, reducing computational overhead and processing time while maintaining high accuracy in threat level determination.
Solution Approach 2:
The system applies local quality by associating specific data parameters and tactical actions with each spatial layer. This localized approach allows machine learning to focus computational resources on relevant parameters for each threat zone, improving processing efficiency while maintaining accurate threat assessment through context-specific parameter evaluation.
Data Source
AI summary
Disclosed is a method for evaluating the level of threat of at least one entity among a plurality of entities in a battlefield environment, the level of threat being evaluated with respect to a reference entity to be protected, the method including the steps of: segmenting the battlefield environment into a plurality of layers; obtaining data representative of a position of the entity with respect to the layers of the battlefield environment; and determining the level of threat of the entity using the obtained data.