Fingerprinting Positioning Accuracy Estimation via Reference Comparison
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Solution Overview
Problem
Fingerprinting positioning algorithms face challenges in accurately estimating location accuracy and confidence due to factors like environmental conditions, measurement quality, and radio cell planning, leading to unpredictable positioning results and a need for improved a priori accuracy estimation.
Innovation Solution
A method and system for generating inaccuracy characteristics by comparing fingerprinting position references with high-accuracy position references to calculate error values, which are then used to determine inaccuracy characteristics associated with geographical regions, enabling improved positioning selection and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fingerprinting positioning algorithms are used to determine UE position, then positioning coverage and basic location estimation are improved, but positioning accuracy and confidence estimation become unpredictable and unreliable
Solution Approach 1:
The system performs preliminary actions by collecting high-accuracy position references (A-GPS, U-TDOA, OTDOA) in advance to create ground truth data. These pre-collected references are stored in databases and used later to evaluate and estimate the accuracy of fingerprinting positioning results, enabling reliable confidence estimation before actual positioning decisions are made.
Solution Approach 2:
The system implements feedback mechanisms by comparing fingerprinting positioning results against pre-collected high-accuracy position references. The error values calculated from these comparisons are fed back to improve the positioning algorithm selection and confidence estimation, creating a closed-loop system that continuously learns and improves positioning reliability.
2Productivity
If multiple positioning algorithms are available for selection, then positioning performance can be optimized, but the complexity of selecting the appropriate algorithm increases
Solution Approach 1:
The system enables self-service by automatically evaluating and selecting positioning algorithms based on pre-stored accuracy characteristics. The network node autonomously compares available positioning results against reference data and selects the most appropriate algorithm without requiring manual intervention or complex real-time analysis, simplifying the selection process while maintaining optimal performance.
Solution Approach 2:
The system performs preliminary evaluation of positioning algorithms by pre-collecting and storing accuracy characteristics for different algorithms and geographical regions. This advance preparation creates lookup tables and databases that enable rapid, simple algorithm selection during actual positioning operations, avoiding complex real-time decision-making.
3Reliability
If a priori accuracy information is provided for positioning algorithms, then positioning selection performance is improved, but additional measurement and processing requirements increase
Solution Approach 1:
The system performs all complex measurements and accuracy evaluations in advance, storing the results as pre-computed accuracy characteristics in databases. During actual positioning operations, the system simply retrieves these pre-stored accuracy values based on the current geographical region and algorithm selection, avoiding the need for complex real-time measurements and processing.
Data Source
AI summary
Presented are methods and apparatus for generating an inaccuracy characteristic for a fingerprinting positioning algorithm calculation associated with a user equipment location. Fingerprinting positioning measurements are compared to high-accuracy positioning measurements and an error value is determined. The error values are accumulated and the inaccuracy characteristic is generated based on an inaccuracy calculation. For areas where insufficient data is available, an inaccuracy characteristic value can be interpolated or extrapolated from adjacent areas containing sufficient data.