Wireless Locationing via Signal Calibration and Weighted Analysis
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Solution Overview
Problem
Existing wireless locationing systems face inaccuracies due to inconsistent data, varying hardware and software configurations, and arterial bias, leading to incorrect location calculations of wireless access points.
Innovation Solution
A method using weighted average analysis and pattern recognition, where devices scan for wireless access points, receive signal strength and identifier information, and query a database to compute location, with the weighted average analysis weighting location points by current signal strength and pattern recognition algorithms associating signal patterns with regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If rudimentary weighted average formulas are used to calculate wireless access point locations, then the calculation process is simple, but the location accuracy deteriorates due to inconsistent data and varying hardware configurations
Solution Approach 1:
The patent transforms the location determination from using raw signal strength values to using calibrated signal strength values that have been adjusted through a calibration process. This calibration involves collecting signal strength data from multiple devices with different hardware configurations, determining calibration factors for each device, and applying these factors to normalize the signal strength readings. This parameter transformation resolves the contradiction by maintaining calculation simplicity while improving location accuracy through data normalization.
Solution Approach 2:
The system performs self-calibration by automatically collecting signal strength data from multiple querying devices, determining calibration factors based on the aggregated data, and updating the database with these calibration factors. This self-service approach allows the system to adapt to varying hardware configurations without manual intervention, maintaining simplicity while improving accuracy over time.
2Adaptability or versatility
If data is collected from multiple devices with different hardware and software configurations, then the system becomes more adaptable, but data inconsistency increases leading to incorrect location calculations
Solution Approach 1:
The patent introduces calibration factors as a parameter transformation that converts inconsistent signal strength readings from different hardware configurations into normalized, comparable values. Each device type receives a calibration factor that adjusts its readings to a common reference frame, thereby maintaining adaptability to various hardware while ensuring data consistency for reliable location calculations.
Solution Approach 2:
The system implements a feedback mechanism where location calculations and signal strength data from multiple devices are continuously aggregated, analyzed, and used to refine calibration factors. This feedback loop allows the system to learn from actual performance data and adjust calibration parameters to compensate for hardware variations, maintaining both adaptability and reliability.
3Speed
If signal strength readings are used directly without calibration, then the processing is faster, but location determination accuracy deteriorates due to arterial bias and signal variations
Solution Approach 1:
The patent applies preliminary calibration to signal strength readings before they are used in location calculations. Calibration factors are pre-computed based on aggregated data from multiple devices and stored in the database. When a location calculation is needed, the system simply retrieves and applies the pre-computed calibration factor, which eliminates the need for complex real-time processing while improving accuracy by removing arterial bias and hardware-specific variations.
Data Source
AI summary
A query device scans radio frequencies for visible transmitting devices. The querying device receives at least a signal strength and identifier information associated with each of the transmitting devices. The list of visible devices is used to query a database containing location information for a plurality of visible devices. The list may be sent to a locationing system that may perform a location analysis on the resulting data to return a location to the query device. The weighted average of the locations returned in the database query may be computed to determine the location of the querying device, with the weight for each of the locations being the current signal strength detected by the querying device. Neural network analysis may also be used to determine the location of the querying device. Learning and seeding operations many also be used to populate the database with location information for transmitting devices.