Automated Access Point Position Estimation with Weighted Measurements
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Solution Overview
Problem
Existing location estimation techniques in wireless communication systems suffer from limited accuracy, especially in indoor environments, and require laborious manual input of network information, leading to inefficiencies and frustration in accessing network entities.
Innovation Solution
A method for automated position estimation using multiple location measurements from coordinating access points, including time of flight, angle of arrival, and carrier phase measurements, with iterative weight determination and confidence analysis to improve accuracy and reduce resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual input of network information is used for location estimation, then network entity access can be achieved, but the process becomes laborious and inefficient
Solution Approach 1:
The system enables automated position estimation by having access points self-report their locations and having the system automatically process location measurements without requiring manual input of network information, thus eliminating the laborious manual entry process while maintaining accurate network entity access
2Measurement precision
If traditional location estimation techniques are used, then basic positioning can be achieved, but accuracy is limited especially in indoor environments
Solution Approach 1:
The system improves location estimation accuracy by utilizing multiple measurement parameters including time of flight, angle of arrival, and carrier phase measurements from multiple access points, and by dynamically adjusting weights assigned to different measurement variants based on their reliability, thereby achieving decimeter or centimeter level accuracy suitable for indoor environments
3Measurement precision
If multiple location measurements from multiple access points are collected, then location accuracy improves to decimeter or centimeter levels, but resource consumption increases
Solution Approach 1:
The system performs additional location measurements and weight determinations only when necessary to achieve the desired confidence level or when triggers indicate improved accuracy is needed, rather than continuously collecting all possible measurements, thereby balancing accuracy improvement with resource conservation
4Measurement precision
If automated position estimation with iterative weight determination is implemented, then processing complexity increases, but location accuracy and confidence assessment improve
Solution Approach 1:
The system dynamically adjusts weights assigned to different location measurements based on their estimated accuracy and reliability, with weights being recalculated iteratively as new measurements become available or when triggers indicate improved accuracy is needed, allowing the processing complexity to adapt to the actual accuracy requirements rather than being statically high
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances location estimation accuracy to decimeter or centimeter levels, expanding its use to indoor scenarios and reducing resource consumption by automating the process, thus improving usability and efficiency.
Implementation Method 1
A method for automated position estimation using multiple location measurements from coordinating access points, including time of flight, angle of arrival, and carrier phase measurements
Data Source
AI summary
Disclosed are systems, apparatuses, methods, and computer-readable media for identifying a locations of a network entities within a network. A method for identifying a location of an (AP) includes receiving a first location measurement associated with a first measurement variant and a second location measurement associated with a second measurement variant from a first AP; receiving a third location measurement associated with the first measurement variant and a fourth location measurement associated with the second measurement variant from a second AP; determining weights associated with the location measurements; determining a first location associated with the first AP and a second location associated with the second AP based on a first weight, a second weight, a third weight, and a fourth weight; determining a confidence of the first location based on a comparison of the first location measurement and the second location measurement.


