Multisensor Change Detection via Statistical Divergence Analysis
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Solution Overview
Problem
Existing single sensor change detection techniques are limited in their ability to detect changes in system behavior over time and fail to utilize the full volume of information from multiple sensors, leading to low detection rates and inability to trace correlation changes between sensors.
Innovation Solution
Multisensory change detection systems that collect data from multiple sensors, use statistical methods to compute dissimilarity scores, and raise alerts when these scores exceed thresholds, enabling the detection of changes in sensor dependence patterns and correlations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single sensor change detection techniques are used, then device complexity is reduced, but detection precision and ability to trace correlation changes deteriorate
Solution Approach 1:
The patent combines multiple sensors into a unified monitoring system that processes signals from several sensors simultaneously. This merging approach enables the system to detect changes in sensor dependence patterns and correlations while maintaining manageable complexity through integrated processing architecture.
Solution Approach 2:
The system performs multiple functions including detecting individual sensor changes, tracing correlation changes between sensors, and identifying process changes. By making the monitoring system multi-functional, it achieves high detection precision across various types of changes without requiring separate specialized systems for each function.
2Use of energy by moving object
If single sensor change detection techniques are used, then computational load is reduced, but detection rate deteriorates
Solution Approach 1:
The system performs preliminary computations by establishing baseline sensor dependence patterns and correlations during normal operation. This preliminary action enables faster real-time detection of deviations from established patterns, achieving high detection rates without excessive computational load during actual monitoring.
Solution Approach 2:
The patent replaces intensive mechanical computation with statistical methods and algorithms that efficiently process sensor data. By using statistical techniques to analyze sensor dependence patterns and correlations, the system achieves high detection rates while maintaining reasonable computational requirements.
3Measurement precision
If multiple sensors are utilized, then detection sensitivity and detection rate improve, but device complexity increases
Solution Approach 1:
The system segments the analysis into distinct components: individual sensor monitoring, pairwise correlation analysis, and overall dependence pattern detection. This segmentation allows multiple sensors to be utilized effectively while managing complexity through modular processing of different aspects of sensor data.
Solution Approach 2:
The patent introduces statistical methods and algorithms as intermediaries that process raw sensor data and transform it into meaningful information about sensor dependence patterns and correlations. These intermediary processing layers enable the system to handle multiple sensors without proportionally increasing overall system complexity.
Data Source
AI summary
Examples of systems and methods for multisensory change detection are generally described herein. A method may include receiving a first set of signals from a first combination of sensors and a second set of signals from a second combination of sensors in a plurality of sensors, and determining a first distribution for the first set of signals and a second distribution for the second set of signals. The method may include estimating a divergence between the first and second distributions using the first and second combinations of sensors, a count of the plurality of sensors, and distances from a plurality of signals in the second set of signals to a first plurality of nearest neighbor signals in the first set of signals and a second plurality of nearest neighbor signals in the second set of signals. The method may include determining whether the divergence exceeds a threshold.