Positioning Integrity Assessment Using Convergence Speed Feedback
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Solution Overview
Problem
Existing positioning technologies lack a method to determine the integrity of location estimates based on the speed of convergence of the location estimation algorithm, which affects the accuracy and reliability of positioning results, particularly in scenarios where the initial guess of the UE's position is far from the true position.
Innovation Solution
A method where the user equipment (UE) determines the convergence speed of the positioning algorithm and reports a relative convergence speed to the location management function (LMF), allowing the LMF to assess the integrity of the location estimate by comparing it to an average convergence speed, thereby enhancing the reliability of positioning integrity decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the initial guess of the UE's position is far from the true position, then the positioning algorithm may fail to converge or require excessive iterations, but the positioning integrity cannot be determined without a convergence speed metric
Solution Approach 1:
The patent applies preliminary action by having the UE determine the convergence speed of the positioning algorithm before final positioning results are accepted. The UE calculates how quickly the algorithm converges to a stable position estimate and reports this convergence speed information to the LMF in advance, allowing the LMF to assess whether the positioning result is reliable before making critical decisions.
Solution Approach 2:
The patent implements feedback by having the UE measure and report the convergence speed of the positioning algorithm back to the LMF. This feedback loop allows the LMF to use the convergence speed information to determine positioning integrity, adjusting its decisions based on the actual performance of the positioning algorithm in current conditions.
2Measurement precision
If the positioning algorithm requires many iterations to converge, then the positioning accuracy may be improved, but the positioning time and computational complexity increase
Solution Approach 1:
The patent applies partial action by having the UE perform a limited number of iterations to estimate convergence speed without completing full convergence. The UE determines the convergence speed by measuring how the position estimate changes over a subset of iterations, which provides sufficient information to assess reliability without requiring the full computational burden of complete convergence.
Solution Approach 2:
The patent changes the parameter being measured from final positioning accuracy to convergence speed. Instead of waiting for the algorithm to fully converge and then measuring accuracy, the system measures the rate of convergence itself as a proxy indicator. This parameter change allows for faster assessment of positioning reliability without requiring complete algorithm convergence.
3Reliability
If the UE reports detailed convergence speed data to the LMF, then the positioning integrity assessment is improved, but the signaling overhead and network traffic increase
Solution Approach 1:
The patent extracts only the essential convergence speed information from the full positioning algorithm execution and reports only this key metric to the LMF. Instead of reporting all intermediate positioning estimates and algorithm states, the UE extracts and reports only the convergence speed parameter, which is sufficient for the LMF to assess positioning integrity without requiring extensive signaling data.
4Reliability
If the LMF uses convergence speed to determine positioning integrity, then the trust in positioning results is improved, but the system complexity for calculating and reporting convergence speed increases
Solution Approach 1:
The patent applies self-service by having the UE independently calculate and determine its own convergence speed without requiring external assistance from the network. The UE autonomously measures how quickly its positioning algorithm converges and generates the convergence speed metric on its own, reducing the computational burden on the network while still providing the LMF with the information needed to assess positioning integrity.
Data Source
AI summary
There is provided an apparatus, and corresponding meth-od, configured to receive, from a network element, an indi-cation that a terminal device is to determine a current convergence speed of a positioning algorithm used to deter-mine a position result of the terminal device; determine the position result of the terminal device using the positioning algorithm; determine the current convergence speed of the positioning algorithm used when determining the position result of the terminal device; determine a relative conver-gence speed of the positioning algorithm, based on the cur-rent convergence speed; and indicate to the network ele-ment at least one of: the relative convergence speed, at least one value obtained based on the current convergence speed, an integrity result of the determined position result, or the determined position result of the terminal device.