RTT Positioning via Access Point Grouping and TCF Compensation
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Solution Overview
Problem
Current high-precision indoor positioning systems face inefficiencies due to the need to estimate separate turnaround calibration functions (TCFs) for each access point, which increases uncertainty and computational costs, especially when using RTT measurements from all access points.
Innovation Solution
Grouping access points into categories based on characteristics, estimating a general TCF for each group, and compensating RTT measurements to exclude less accurate access points, thereby reducing uncertainty and computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate TCFs are estimated for each access point, then measurement precision may be maintained, but device complexity and computational load increase
Solution Approach 1:
The patent segments access points into multiple groups based on characteristics such as deployment environment, hardware configuration, or signal properties. Each group shares a common TCF value, reducing the number of separate TCF estimations required. This segmentation maintains measurement precision within groups while significantly reducing device complexity and computational load compared to estimating separate TCFs for each individual access point.
Solution Approach 2:
The patent merges multiple access points into groups that share common TCF characteristics. By combining access points with similar properties into single groups, the system estimates one TCF value representing the entire group rather than separate TCFs for each access point. This merging approach reduces computational complexity while maintaining adequate measurement precision through group-based calibration.
2Measurement precision
If RTT measurements from all access points are used, then positioning accuracy may be maintained, but uncertainty increases due to multiple TCF variations
Solution Approach 1:
The patent segments access points into distinct groups based on shared characteristics, allowing the system to select measurements from groups with known and consistent TCF values. This segmentation reduces measurement uncertainty by excluding access points with variable or unknown TCF characteristics, while still maintaining positioning accuracy through sufficient numbers of reliable access points in each group.
Solution Approach 2:
The patent applies local quality by assigning different TCF characteristics to different groups of access points based on their specific properties. Rather than using a uniform approach for all access points, the system tailors TCF estimation to each group's characteristics, improving reliability by selecting measurements from groups with favorable and well-understood TCF properties.
3Productivity
If access points are grouped by characteristic, then computational efficiency improves, but the number of access points available for positioning may decrease
Solution Approach 1:
The patent segments access points into groups that balance computational efficiency with sufficient quantity for accurate positioning. By creating multiple groups rather than a single large group, the system maintains computational efficiency through reduced TCF estimation complexity while ensuring each group contains enough access points to provide reliable positioning data. The segmentation strategy optimizes the trade-off between processing load and available measurement sources.
Data Source
AI summary
Systems, apparatus and methods for estimating a location of a mobile device are presented. Before computing a location estimate, the mobile device groups a plurality of access points into two or more categories (for example, a first list of access points having a first characteristic and a second list of access points having a second characteristic). Round-trip time (RTT) measurements are computed for access points in the first list. A Short Interframe Space (SIFS) value may be determined for each access point in the first list or generally SIFT representing the first list as a whole. The RTT measurements are compensated with the appropriate SIFS value. The mobile device then computes its location or position fix estimate using the compensated RTT values while excluding less accurate RTT values from other access points. As a result, the location estimate eliminates adverse influent from some access points.