Weapon Detection via Multi-Sensor Fusion and Anomalous Behavior Modeling
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Solution Overview
Problem
Existing weapons detection systems face challenges such as high operational costs, frequent maintenance needs, limited detection capabilities, and issues with false alarms or missed threats, which hinder their ability to provide reliable real-time results.
Innovation Solution
A system utilizing artificial intelligence that integrates multiple sensor types (optical, infrared, RADAR, LIDAR) for sensor fusion, combined with an anomalous behavior model and machine learning networks (including CNNs, GANs, RNNs) to detect potential threats such as firearms, knives, or explosive devices, and to refine confidence levels based on real-time data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional weapons detection systems are used, then detection capability is provided, but false alarms and missed threats occur frequently
Solution Approach 1:
The patent combines multiple sensor types (optical, infrared, radar, acoustic) into a unified detection system. By fusing data from these diverse sensors, the system achieves more reliable threat detection with reduced false alarms, as each sensor type compensates for the weaknesses of others
Solution Approach 2:
The patent introduces AI/machine learning algorithms as an intermediary layer between raw sensor data and threat identification. This intermediary processes and analyzes sensor inputs, improving measurement precision in identifying actual threats versus false positives
2Reliability
If advanced detection systems are deployed, then detection capability is improved, but operational costs increase
Solution Approach 1:
The patent divides the detection system into modular sensor components (optical, infrared, radar, acoustic) that can be selectively activated. This segmentation allows the system to maintain high detection reliability while reducing operational costs by only activating necessary sensors based on situational requirements
Solution Approach 2:
The system employs periodic scanning and event-triggered activation rather than continuous full-power operation of all sensors. This periodic action maintains detection reliability while significantly reducing energy consumption and operational costs
3Speed
If real-time detection is implemented, then response speed is improved, but system complexity increases
Solution Approach 1:
The patent pre-processes and filters sensor data in real-time, preparing information before full analysis is required. This preliminary action enables faster response times while managing system complexity by handling basic processing tasks continuously and reserving complex analysis for when needed
Data Source
AI summary
A system for detecting a potential threat includes a processor and a memory coupled to the processor. The memory has instructions stored thereon, which when executed by the processor, cause the system to access a first signal from a first sensor system, the first signal including an optical signal, an infrared signal, a radio detection and ranging (RADAR) signal, or a light detection and ranging (LIDAR) signal; generate a first fused signal stream, using sensor fusion, based on the first signal; provide a confidence level of a potential threat, based on the first fused signal stream, using an anomalous behavior model; determine that the provided confidence level of the potential threat exceeds a predetermined threshold; and output an alert indicating a condition of the potential threat.