Sensor Detection Probability Fusion for Threat Analysis
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Solution Overview
Problem
The limited range and availability of sensor platforms hinder the detection of objects or events, leading to incomplete and delayed information for war fighters, making it difficult to respond effectively to imminent threats.
Innovation Solution
A method of fusing sensor detection probabilities, including accuracy and availability probabilities, to determine the effectiveness of existing resources and decide on the deployment or removal of additional resources, allowing for the most effective combination of sensors and platforms to detect and track targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensor platforms are utilized, then detection capability is improved, but system complexity and resource requirements increase
Solution Approach 1:
The patent combines multiple sensor platforms and their detection probabilities into a unified fusion system. The system integrates data from diverse sensors (electromagnetic, acoustic, seismic, etc.) and merges their individual detection probabilities to compute an overall detection probability, thereby improving reliability while managing complexity through systematic integration.
Solution Approach 2:
The detection probability fusion system serves multiple functions: it evaluates existing sensor platform effectiveness, determines optimal resource allocation, assesses additional platform benefits, and supports automated decision-making. This multi-functional approach allows a single system to address various aspects of detection and resource management.
2Measurement precision
If more sensor platforms are deployed, then detection accuracy is improved, but resource allocation efficiency decreases
Solution Approach 1:
The system changes the parameter of resource allocation from fixed deployment to dynamic optimization based on detection probability calculations. By computing the expected effectiveness of additional platforms and comparing it against resource costs, the system determines optimal resource allocation that maintains detection accuracy while improving efficiency.
Solution Approach 2:
The system implements feedback through automated decision aids that use detection probability fusion results to guide resource allocation decisions. The fusion outcomes feed back into resource deployment strategies, allowing continuous optimization of both detection accuracy and resource efficiency based on actual performance data.
3Loss of time
If detection probability fusion is implemented, then decision-making speed is improved, but computational complexity increases
Solution Approach 1:
The computational process is segmented into distinct modules: individual sensor detection probability calculation, data fusion integration, expected effectiveness computation, and decision aid generation. This segmentation allows complex calculations to be performed in manageable stages, improving computational efficiency and decision-making speed while reducing overall complexity.
Solution Approach 2:
The system performs preliminary calculations of detection probabilities and expected effectiveness before actual resource allocation decisions are made. By pre-computing these parameters and storing them for quick retrieval, the system enables rapid decision-making without repeating complex calculations in real-time scenarios.
Data Source
AI summary
A method of fusing sensor detection probabilities. The fusing of detection probabilities may allow a first force to detect an imminent threat from a second force, with enough time to counter the threat. The detection probabilities may include accuracy probability of one or more sensors and an available time probability of the one or more sensors. The detection probabilities allow a determination of accuracy of intelligence gathered by each of the sensors. Also, the detection probabilities allow a determination of a probable benefit of an additional platform, sensor, or processing method. The detection probabilities allow a system or mission analyst to quickly decompose a problem space and build a detailed analysis of a scenario under different conditions including technology and environmental factors.