Positioning Measurement Weighting for NLOS Accuracy
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Solution Overview
Problem
Existing positioning technologies face accuracy degradation due to inaccurate measurements, particularly in non-line-of-sight conditions, which can distort location estimates and require costly data labeling for supervised methods.
Innovation Solution
A method to determine a weight for positioning measurements by calculating differences between target channel features and reference values, allowing for improved positioning accuracy without needing multiple measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple positioning measurements are combined to estimate position, then positioning coverage is improved, but positioning accuracy degrades due to inaccurate measurements in non-line-of-sight conditions
Solution Approach 1:
The patent applies local quality by assigning different weights to different positioning measurements based on their individual reliability. Each measurement is evaluated using channel features (such as signal strength, delay spread, kurtosis) that locally characterize the propagation conditions of that specific measurement path. Measurements with better local channel conditions receive higher weights, while those with poor conditions (likely NLOS) receive lower weights, thus maintaining high accuracy across diverse positioning scenarios.
Solution Approach 2:
The patent changes parameters by introducing a weighting mechanism that dynamically adjusts the contribution of each positioning measurement. Instead of treating all measurements equally, the system modifies the effective parameter (weight) of each measurement based on channel feature analysis. This allows the system to adapt to varying measurement qualities and maintain overall positioning accuracy even when some measurements are degraded by non-line-of-sight conditions.
2Measurement precision
If supervised methods are used to correct inaccurate measurements, then positioning accuracy is improved, but data labeling costs increase
Solution Approach 1:
The patent implements self-service by enabling the system to automatically evaluate and weight positioning measurements without requiring external data labeling or supervised training. The channel features (signal strength, delay spread, kurtosis) are computed directly from the received positioning signals, and the weighting algorithm autonomously determines which measurements to trust. This unsupervised approach eliminates the need for costly manual data labeling while maintaining the ability to identify and downweight inaccurate NLOS measurements.
3Device complexity
If all positioning measurements are treated equally, then device complexity is reduced, but positioning accuracy degrades due to inaccurate measurements
Solution Approach 1:
The patent applies partial action by selectively processing only the relevant aspects of each positioning measurement for weighting purposes. Instead of performing complex analysis on all measurement parameters, the system focuses on computing a limited set of channel features (signal strength, delay spread, kurtosis) that are sufficient to distinguish LOS from NLOS conditions. This selective processing approach maintains relatively low device complexity while achieving the goal of identifying and weighting accurate measurements more effectively than treating all measurements equally.
Data Source
AI summary
According to example embodiments of the present disclosure, a method for weighting positioning measurements is proposed. A difference between a measurement and a reference value is determined based on one or more channel features associated with the measurement and the reference value. A weight for the measurement is determined based on the difference between the measurement and the reference value. In this way, positioning accuracy can be improved. Moreover, it does not require multiple measurements when assigning the weight for the measurement.