Multi-Sensor Data Fusion for False Alarm Reduction and Localization
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Solution Overview
Problem
Current security systems fail to collectively process sensor data from multiple intrusion sensors, leading to unnecessary alarms and inefficiencies in alarm verification and localization.
Innovation Solution
A system and method for sensor data fusion that involves collecting and processing raw data from multiple sensors, applying collective threshold logic to determine alarms, and utilizing big data analytics to confirm and localize alarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If individual detectors process alarm data independently with individual thresholds, then each detector can quickly detect alarms, but false alarms increase and detection accuracy decreases
Solution Approach 1:
The patent combines data from multiple detectors through a control panel that performs fusion processing. Instead of independent detection, the system merges sensor data, fault messages, and alarm levels from multiple detectors to make collective alarm decisions, reducing false alarms while maintaining detection accuracy.
Solution Approach 2:
The control panel serves as an intermediary between individual detectors and the alarm output. It receives raw data and processed information from multiple detectors, applies fusion algorithms and collective threshold logic, then generates final alarm decisions, thereby improving reliability without requiring complex individual detector processing.
2Reliability
If multiple sensors are deployed to improve detection coverage, then alarm detection coverage increases, but false alarms and processing inefficiency increase
Solution Approach 1:
The system merges data from multiple sensors deployed across different areas through the control panel. By combining sensor readings, fault messages, and alarm levels from multiple detectors in a unified processing framework, the system maintains comprehensive detection coverage while improving verification efficiency through collective analysis.
Solution Approach 2:
The control panel implements feedback mechanisms where alarm information from multiple detectors is continuously monitored and cross-verified. The system uses feedback loops to compare alarm signals against collective threshold logic and fusion results, enabling efficient verification of alarms across multiple sensors without linearly increasing processing complexity.
3Measurement precision
If individual detectors use fixed threshold logic, then detection is simple and fast, but false alarms occur and advanced detection is limited
Solution Approach 1:
The system changes from fixed individual thresholds to dynamic collective thresholds. The control panel calculates alarm levels based on fused data from multiple detectors, adjusting effective thresholds based on the combined state of all sensors. This allows more precise detection by considering the collective behavior of the sensor network rather than relying on static individual thresholds.
Solution Approach 2:
The control panel performs preliminary fusion processing of raw sensor data before applying threshold logic. By pre-processing and combining data from multiple detectors upfront, the system creates a more informed basis for alarm decisions, improving detection precision while managing complexity through structured preliminary analysis rather than complex real-time individual detector logic.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances alarm detection accuracy by reducing false alarms and improving localization through collective processing and big data analysis, thereby optimizing security system responses.
Implementation Method 1
conveying raw microwave data and/or passive infrared (PIR) data from the plurality of detectors to the control panel
Implementation Method 2
conveying raw microwave data and/or passive infrared (PIR) data from the plurality of detectors to the control panel
Data Source
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AI summary
A data analysis system and approach having sensors, a collective processing mechanism connected to the sensors, and a threshold logic panel connected to the collective processing mechanism. Raw data from the sensors may be subject to collective processing and collective threshold logic analysis. The collective processing mechanism and the threshold logic panel may be situated outside of or in the cloud. Big data analytics may be performed on the data. The sensors may be homogeneous or heterogeneous. Consequently, there may be data fusion for false alarm reduction and advanced alarm detection, and application of big data analysis. Raw data may be used for determining positional information.