Wireless Positioning Error Modeling for Integrity Warnings
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Solution Overview
Problem
Existing positioning systems lack the ability to accurately model and mitigate error sources, leading to reduced integrity and reliability in positioning data, which affects the trust in location services.
Innovation Solution
Implementing a system that models error sources through symmetrical and asymmetrical paired overbounding models, using characteristic parameters such as standard deviations and means to enhance the integrity and reliability of positioning methods, including error sources like timing, location, and angle-related errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If error source modeling with characteristic parameters is implemented, then positioning integrity and reliability are improved, but device complexity and processing requirements increase
Solution Approach 1:
The error source modeling is segmented into distinct characteristic parameters (mean, standard deviation, skewness, kurtosis) that can be independently calculated, transmitted, and processed. This segmentation allows the complex error characterization to be broken down into manageable components that improve positioning integrity without overwhelming system complexity.
Solution Approach 2:
Characteristic parameters serve as intermediaries between the raw error sources and the positioning calculations. Instead of directly processing complex error sources, the system uses these parameters as mediators to convey error information efficiently through assistance data messages, improving reliability while maintaining manageable complexity.
2Measurement precision
If comprehensive error source parameters are transmitted in assistance data, then positioning accuracy is improved, but message size and processing overhead increase
Solution Approach 1:
The system changes the parameter representation by using statistical characteristics (mean, standard deviation, skewness, kurtosis) instead of transmitting raw error data. This parameter transformation compresses complex error information into concise numerical values that maintain positioning accuracy while minimizing message size and transmission overhead.
3Reliability
If error source modeling is implemented, then positioning integrity is improved, but computational resources and processing time are consumed
Solution Approach 1:
Error source modeling and characteristic parameter calculation are performed in advance by network entities (LMF, gNB) before positioning sessions. This preliminary action prepares error characterization data that can be reused across multiple positioning operations, improving positioning integrity while minimizing real-time computational energy consumption by UEs.
Data Source
AI summary
Presented are systems and methods for positioning. A method includes sending, by a core network entity to a first wireless communication entity, a first message including measurement and/or assistance data for positioning, wherein the assistance data includes no or one or more characteristic parameters associated with a model of an error source. Another method includes receiving, by a core network entity from a second wireless communication entity, a second message including assistance data for positioning, wherein the assistance data includes one or more characteristic parameters associated with a model of an error source.