AECID Fingerprinting Polygon Shrinking for Positioning Accuracy
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Solution Overview
Problem
Current polygon shrinking routines in fingerprinting positioning technology often find local minima instead of global minima, leading to suboptimal positioning accuracy in cellular communications networks.
Innovation Solution
The method involves establishing a cell relation configuration for user equipment, performing high-precision position determinations, clustering results, and associating an area definition with the clustered results by shrinking the polygon towards a contraction point situated in the interior of the cluster, ensuring accurate AECID fingerprinting positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional polygon shrinking routines are used in fingerprinting positioning, then the positioning process can be completed, but the algorithm finds local minima instead of global minima, resulting in suboptimal positioning accuracy
Solution Approach 1:
The patent applies preliminary action by pre-processing the cluster of position measurements to identify and remove outliers before performing polygon shrinking. This preparatory step ensures that the subsequent optimization algorithm starts with clean data, preventing convergence to local minima and improving the reliability of finding the global minimum for accurate positioning.
Solution Approach 2:
The patent implements feedback by using an iterative optimization process where the polygon shrinking algorithm continuously evaluates its progress and adjusts its approach based on the current state. The algorithm uses feedback from each iteration to guide the next step, ensuring convergence to the global minimum rather than getting trapped in local minima, thereby improving positioning accuracy.
2Measurement precision
If the contraction point is not properly selected within the cluster, then the polygon shrinking can be performed, but the positioning accuracy deteriorates due to suboptimal AECID polygon computation
Solution Approach 1:
The patent applies self-service by implementing an automatic contraction point selection mechanism that uses the geometric properties of the clustered position measurements themselves. The algorithm identifies the contraction point based on the cluster's inherent characteristics (such as the centroid or most dense region), eliminating the need for manual intervention and ensuring the contraction point is always optimally positioned within the cluster for accurate positioning.
Data Source
AI summary
A method for providing position determination assisting data comprises repetitions of establishing (210) of a cell relation configuration for a user equipment and performing (212) of a high-precision position determination for the user equipment. Results of the determinations belonging to the same cell relation configuration are clustered (214) in separate clustered results. An area definition is associated (220) with the clustered results by enclosing (221) the clustered results by a polygon, shrinking (222) the polygon by moving corners towards a contraction point and defining (223) the area definition as a shrunk polygon comprising a predetermined fraction of the clustered results. The contraction point is selected to be situated within the clustered results. The method also comprises creating (230) of position determination assisting data comprising a relation between the cell relation configurations and the associated area definitions. An arrangement for providing position determination assisting data is also presented.