Sensor Data Association via Synchronized Change Point Probability
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Solution Overview
Problem
Existing systems for tracing user positions using multiple sensors face accuracy issues when associating data from different types of sensors, particularly with wireless terminals that are not always carried by the user, leading to erroneous associations and reduced tracing accuracy.
Innovation Solution
A data processing device and method that detect change points in data series from various sensors, calculate the occurrence probability of synchronized change points, and associate identifiers based on this probability to determine the type of identification target, thereby improving association accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data from multiple types of sensors are associated based on order of times, then position tracking coverage is improved, but tracing accuracy deteriorates due to erroneous associations
Solution Approach 1:
The patent changes the association parameter from simple time order to time difference magnitude. By calculating the absolute time difference between sensor detections and comparing it against a threshold, the system identifies valid associations more accurately, preventing erroneous matches while maintaining comprehensive tracking coverage.
Solution Approach 2:
The patent introduces time difference calculation as an intermediary criterion between raw sensor data and final position tracking results. This intermediate step filters out spurious associations by requiring that detected objects from different sensors occur within a acceptable time window, thereby improving tracing accuracy without sacrificing coverage.
2Productivity
If identifiers are associated without verification, then data processing speed is improved, but association accuracy deteriorates
Solution Approach 1:
The patent performs preliminary verification by calculating time differences between sensor detections before finalizing associations. This pre-check mechanism quickly eliminates obviously mismatched pairs, allowing the system to maintain high processing speed while improving association accuracy through minimal additional computation.
Data Source
AI summary
A data processing device (10) includes: a detection unit (11) that detects change points of a first data series including a first identifier and change points of a second data series including a second identifier; a calculation unit (12) that calculates an occurrence probability that the change points of the first data series and the change points of the second data series occur in synchronization with each other; an association unit (13) that associates the first identifier and the second identifier as identifiers related to a tracing target on the basis of the occurrence probability; and a determination unit (14) that determines a type of an identification target of the associated first or second identifier on the basis of the occurrence probability.