Indoor Wireless Positioning Using RSSI Variability Fingerprints
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Solution Overview
Problem
Existing indoor positioning methods using wireless communication devices face challenges in achieving high accuracy due to variability in received signal strength indicator (RSSI) caused by structural features, floating population, and positioning time, making it difficult to determine precise locations indoors.
Innovation Solution
An indoor wireless positioning method that utilizes a primary location determining unit to establish an initial location based on RSSI fingerprints and a secondary location determining unit to refine this location using RSSI variability features, updating the position based on variability fingerprints in a pre-constructed database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fingerprint technique is used for indoor positioning, then positioning accuracy is improved, but reliability deteriorates due to high variability from floating population, structural features, and positioning time
Solution Approach 1:
The patent applies dynamics by transitioning from a static fingerprint database to a dynamic system that adapts to changing environmental conditions. The variability feature extraction mechanism continuously monitors and adapts to changes in floating population, structural features, and positioning time, making the positioning system reliable despite environmental variability.
Solution Approach 2:
The patent changes the parameter space by not only using RSSI values but also extracting variability features (standard deviation, mean, variance) of RSSI signals. This parameter transformation allows the system to characterize environmental conditions and improve reliability while maintaining positioning accuracy.
2Reliability
If multiple RSSI measurements and variability analysis are performed, then positioning reliability is improved, but device complexity increases
Solution Approach 1:
The patent segments the positioning system into distinct functional modules: initial positioning module, additional RSSI reception module, variability feature extraction module, and variability fingerprint matching module. This segmentation manages complexity by organizing functions into manageable, independent components.
Solution Approach 2:
The patent applies preliminary action by pre-calculating and storing variability fingerprints in the database before actual positioning operations. During positioning, the system only needs to extract variability features and perform matching, significantly reducing real-time computational complexity while maintaining reliability.
3Measurement precision
If variability fingerprint database is used, then positioning accuracy is improved in high variability environments, but loss of information increases due to additional processing requirements
Solution Approach 1:
The patent extracts only the essential variability features (standard deviation, mean, variance) from the full RSSI signal data, separating the critical information needed for positioning from redundant data. This extraction minimizes information loss while capturing the essential characteristics of signal variability for accurate positioning.
Data Source
AI summary
An indoor wireless positioning method includes receiving initial received signal strength indicator (RSSI) information from a reception unit; searching for a fingerprint matching measured initial RSSI information in a fingerprint database; determining a first location corresponding to the retrieved fingerprint to be an initial location of the reception unit; receiving additional RSSI information from the reception unit; extracting features of variability between the additional RSSI information and the initial RSSI information; searching for variability fingerprints matching the initial location and extracted RSSI variability features in a variability fingerprint database; and updating a second location to a current location of the reception unit when the second location corresponding to the retrieved variability fingerprint is different from the initial location, wherein the second location is a location in a candidate area within a preset distance range centered on the first location.