Wireless Location Calibration Using Radio Signal Distribution Differences
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Solution Overview
Problem
Current wireless location determination systems face challenges in efficiently identifying new sample points due to inconsistent grid sizes and the double-role attribute of border line sample points, leading to ineffective error distance estimation and neglect of sample points in certain regions.
Innovation Solution
A method that calculates the stability and difference in radio signal distributions between locations, using a penalty mechanism and calibration index to determine new sample points without relying on decision rules, addressing the issues of inconsistent grid sizes and border line sample points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If error distance estimation is used to determine new sample points, then the location determination system can identify points for calibration, but the selection becomes ineffective due to inconsistent grid sizes and border line sample point attributes
Solution Approach 1:
The patent changes the parameter used for sample point selection from error distance expectation to calibration index. The calibration index is calculated based on signal stability and distribution difference, which are more reliable parameters that are independent of grid size and border line effects. This parameter change resolves the contradiction by providing an effective selection criterion that works consistently across different grid configurations.
Solution Approach 2:
The patent introduces a penalty mechanism that effectively discounts or ignores border line sample points and regions with inconsistent grid sizes. By applying penalties to these problematic areas, the system can focus calibration resources on more valuable sample points, making the selection process more effective without requiring complex handling of border line cases.
2Productivity
If random mapping decision rule is used, then the system can process location data, but error distance expectation cannot differentiate locations based on received signals
Solution Approach 1:
The patent introduces feedback mechanisms where the calibration index calculation incorporates signal stability and distribution difference information. This feedback loop allows the system to use received signal characteristics to guide sample point selection, thereby preventing information loss while maintaining processing efficiency. The calibration index serves as a feedback signal that directs calibration efforts to the most informative locations.
3Reliability
If extensive radio data collection is performed at all sample points, then accurate probabilistic models can be established, but the process becomes impractical due to the large number of sample points
Solution Approach 1:
The patent applies partial action by collecting radio data only at a selected subset of sample points rather than all sample points. The calibration index-based selection identifies the most informative sample points where data collection will have the greatest impact on model accuracy. This approach achieves sufficient model reliability with significantly reduced data collection time and resources.
Solution Approach 2:
The patent performs preliminary calculation of calibration indices for all sample points before actual data collection. This preliminary action identifies which sample points should be prioritized for data collection, allowing the system to focus resources on the most valuable locations and avoid wasting time collecting data at less informative points.
Data Source
AI summary
Disclosed is a method and apparatus for determining the new sample points of the location determination system in a wireless environment. In the training phase, after knowing the distribution of the observed radio signal of a location for a target device, the uncertainty of the probability distribution of the received radio signal is analyzed by a location probability distribution model. The radio signal distribution differences are calculated, and the penalties between the location in question and its nearby locations are also calculated, thereby determining whether a location is the candidate of new sample points or the recalibration points. This invention has nothing to do with decision rules and will not undergo the problems of choosing inappropriate decision rules. It also provides a penalty model for generating penalty of error prediction from one location to another location, which can as well easily mitigate the problem on the sample points with the double-role attribute of the border line.