Ranging Measurement Weighting for Positioning Accuracy
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Solution Overview
Problem
Current wireless communication systems face challenges in accurately combining ranging measurements taken at different bandwidths for positioning, as existing methods either assign zero weights to lower bandwidth measurements or use equal weights for all, leading to suboptimal ranging performance.
Innovation Solution
A method that determines a weighted mean of ranging measurements based on the bandwidth and type of response message, using inverse relative error variances as weights to optimize ranging accuracy, considering signal-to-noise ratio, spatial diversity, RF sensing results, and motion state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If equal weights are assigned to all ranging measurements regardless of bandwidth, then the combining process is simple, but the positioning accuracy deteriorates
Solution Approach 1:
The patent changes the weighting parameter from a fixed equal value to a dynamic value that depends on measurement bandwidth and signal-to-noise ratio. The weight for each ranging measurement is calculated as w_i = (SNR_i / bandwidth_i) / sum(SNR_j / bandwidth_j), transforming the combining process from a simple arithmetic mean to an optimized weighted mean that adapts to different measurement conditions.
2Device complexity
If zero weights are assigned to lower bandwidth measurements, then the calculation is straightforward, but the positioning accuracy deteriorates due to loss of useful measurement data
Solution Approach 1:
The patent applies local quality by assigning different weights to different measurements based on their specific characteristics (bandwidth and SNR). Instead of a global zero-weight policy for low bandwidth measurements, each measurement receives a localized weight proportional to its quality metric (SNR/bandwidth), allowing useful information from all bandwidths to be utilized optimally.
3Measurement precision
If optimal weights based on SNR and bandwidth are used, then positioning accuracy improves, but the calculation complexity increases
Solution Approach 1:
The system performs self-service by automatically calculating optimal weights using the available measurement data itself. The weight calculation w_i = (SNR_i / bandwidth_i) / sum(SNR_j / bandwidth_j) uses only the SNR and bandwidth values already obtained during the ranging process, requiring no external parameters or complex iterative optimization, thus achieving high accuracy with minimal additional complexity.
Data Source
AI summary
Disclosed are techniques for positioning. In an aspect, a positioning entity obtains a plurality of pairs of timing measurements based on a plurality of pairs of ranging messages exchanged on a plurality of bandwidths, determines a plurality of range measurements based on the plurality of pairs of timing measurements, each range measurement of the plurality of range measurements based on one pair of the plurality of pairs of timing measurements, determines a weight for each range measurement of the plurality of range measurements based at least on a bandwidth of the plurality of bandwidths of a pair of timing measurements of the plurality of pairs of timing measurements used to determine the range measurement, and determines a weighted mean of the plurality of range measurements based at least on the plurality of range measurements and weights of the plurality of range measurements.