Wireless Positioning Iterative Attenuation Refinement
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Solution Overview
Problem
Existing wireless communication network positioning methods fail to accurately account for signal losses due to obstacles like vehicles and buildings, leading to inaccuracies in estimating the position of mobile devices within indoor environments.
Innovation Solution
A method that iteratively refines signal strength attenuation calculations by defining an uncertainty area around the estimated position, using a weighted average of previous attenuation values and probability distributions to improve accuracy, allowing for better consideration of unmodeled obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If radio signal propagation simulation tools are used to generate RF signals strength maps, then positioning can be performed in outdoor environments, but accuracy deteriorates in indoor environments due to unmodeled obstacles like vehicles and buildings
Solution Approach 1:
The system dynamically adjusts the attenuation parameter through iterative refinement. Instead of using a static attenuation value from propagation models, the system updates the attenuation parameter based on measured signal strengths and uncertainty areas across multiple iterations, adapting to the actual indoor environment conditions that differ from outdoor propagation models
Solution Approach 2:
The system implements feedback by using measured signal strengths to refine the attenuation parameter. The measured signal strengths are compared with expected values, and the difference is used to update the attenuation parameter in subsequent iterations, creating a closed-loop system that continuously improves positioning accuracy
2Productivity
If standard RF fingerprint matching is used, then positioning is computationally efficient, but positioning accuracy deteriorates due to unaccounted signal losses from obstacles
Solution Approach 1:
The system performs preliminary action by pre-calculating uncertainty areas and expected signal strengths for multiple candidate positions before final position determination. This allows the iterative refinement process to work with pre-computed values, maintaining computational efficiency while improving accuracy
Solution Approach 2:
The system changes the attenuation parameter iteratively based on measured signal strengths. By adjusting this key parameter rather than changing the entire positioning algorithm, the system maintains computational efficiency while significantly improving positioning accuracy in indoor environments
3Measurement precision
If field measurement campaigns are conducted to improve RF signals strength maps, then outdoor positioning accuracy is improved, but indoor positioning remains inaccurate due to unmodeled obstacles
Solution Approach 1:
The system dynamically adapts to indoor environments by iteratively refining the attenuation parameter based on actual measurements. Instead of relying on static field measurement data that cannot capture variable indoor obstacles, the system continuously adjusts parameters to adapt to the specific indoor environment
Solution Approach 2:
The system introduces an intermediary iterative refinement process that bridges the gap between outdoor propagation models and indoor reality. This intermediary process uses measured signal strengths to compute uncertainty areas and refine attenuation parameters, enabling accurate indoor positioning without requiring extensive indoor field measurements
Data Source
AI summary
A method of identifying a position of a user equipment within a wireless communication network includes: a) providing expected radio signal strengths produced by at least one radio communication station on each of plural elementary area elements in which a geographic area is subdivided; b) defining an initial attenuation experienced by the radio signals to the user equipment whose position is to be identified; c) obtaining measured radio signal strength measurements of the radio signals provided to the user equipment whose position is to be identified; d) determining an estimated elementary area element corresponding to the position of the user equipment whose position is to be identified based on the expected radio signal strengths, the initial attenuation and the radio signal strength measurements, and e) computing a final attenuation based on the estimated elementary area element. The operations b)-e) are iterated at least twice.