Instrumented Security Barrier with Correlated Sensor Fusion
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Solution Overview
Problem
Existing security systems fail to effectively detect intrusions and reduce false alarm rates, as they often rely on single sensors and lack integration with physical barriers, leading to inadequate identification of disturbances and high nuisance and false alarm frequencies.
Innovation Solution
An intrusion delaying barrier system that combines multiple sensors of different types physically interconnected with primary and secondary structures, utilizing machine learning networks to correlate sensor data and discriminate between genuine intrusions and nuisance or false alarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensors of different types are physically interconnected with primary and secondary structures, then the probability of detecting intrusions is enhanced, but the device complexity increases
Solution Approach 1:
The barrier system is divided into primary structures (concrete blocks with tie-bars) and secondary structures (fence, wall, or other barrier elements). Sensors are segmented and mounted on different structural components, allowing independent installation and maintenance while collectively providing comprehensive intrusion detection coverage.
Solution Approach 2:
Multiple sensors of different types (seismic, acoustic, optical, etc.) are physically interconnected and integrated into a unified monitoring system. The sensors are mounted on both primary and secondary structures, merging their detection capabilities to enhance overall intrusion detection reliability while reducing false alarms through correlated analysis.
2Reliability
If machine learning networks are used to correlate sensor data, then false alarm rates are reduced, but the loss of time for data processing increases
Solution Approach 1:
The machine learning network is pre-trained with extensive sensor data patterns representing both genuine intrusions and nuisance conditions. This preliminary training enables the system to quickly classify new sensor inputs without requiring extensive real-time computation, thus reducing false alarms while minimizing processing time delays.
Solution Approach 2:
The system continuously monitors sensor correlations and uses machine learning algorithms to provide feedback on detected patterns. The feedback mechanism allows the system to learn from past false alarms and genuine intrusions, progressively improving its ability to distinguish between them while maintaining rapid response times through optimized decision thresholds.
Data Source
AI summary
An intrusion delaying barrier includes primary and secondary physical structures and can be instrumented with multiple sensors incorporated into an electronic monitoring and alarm system. Such an instrumented intrusion delaying barrier may be used as a perimeter intrusion defense and assessment system (PIDAS). Problems with not providing effective delay to breaches by intentional intruders and/or terrorists who would otherwise evade detection are solved by attaching the secondary structures to the primary structure, and attaching at least some of the sensors to the secondary structures. By having multiple sensors of various types physically interconnected serves to enable sensors on different parts of the overall structure to respond to common disturbances and thereby provide effective corroboration that a disturbance is not merely a nuisance or false alarm. Use of a machine learning network such as a neural network exploits such corroboration.