Mobile Node Localization Using Adaptive Gaussian Noise Modeling
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Solution Overview
Problem
Existing methods for estimating the location of mobile network nodes in wireless communication networks suffer from inaccuracies due to unknown noise distributions, particularly in scenarios with limited calibration and few static network nodes, leading to inefficient localization performance.
Innovation Solution
A method utilizing a Gaussian mixture noise model with momentum-based updates and merging, combined with Kullback-Leibler divergence checks, to iteratively refine location estimates based on range measurements without extensive calibration, ensuring high accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing location estimation methods are used with limited calibration and few static network nodes, then the device complexity and calibration requirements are reduced, but the location estimation accuracy deteriorates
Solution Approach 1:
The patent performs preliminary calibration-free operations by using static network nodes with known locations to establish initial range measurements. The system pre-processes noise samples and determines initial parameters of Gaussian mixture noise models before actual location estimation, eliminating the need for extensive on-site calibration while maintaining accuracy.
Solution Approach 2:
The patent implements iterative feedback mechanisms where location estimates are continuously refined using range measurements from static network nodes. The system updates noise model parameters based on observed measurements and feeds these improved parameters back into the location estimation process, progressively enhancing accuracy without additional calibration.
2Measurement precision
If iterative refinement of noise models is performed, then the location estimation accuracy is improved, but the computational time and processing complexity increase
Solution Approach 1:
The patent employs dynamic Gaussian mixture noise models that adapt their parameters iteratively based on observed range measurements. The noise model parameters are updated in real-time as new measurements become available, allowing the system to refine location estimates dynamically without requiring exhaustive pre-computation or fixed iterative processes.
Solution Approach 2:
The patent changes noise model parameters iteratively by determining initial parameters from noise samples and then refining these parameters based on range measurements. The system adjusts parameters such as means and covariances of the Gaussian mixture components to better fit the actual measurement data, improving accuracy while controlling computational overhead through parameter optimization.
Data Source
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AI summary
A method for estimating a location of at least one mobile network node comprises the steps of performing (100) initial range measurements between at least two static network nodes in a pairwise manner, and determining (101) an initial location estimate with respect to the at least one mobile network node on the basis of said initial range measurements. It also has the steps of determining (102) an estimate for corresponding noise samples on the basis of said initial location estimate and/or a respective location of at least one of the at least two static network nodes, estimating (103) corresponding initial parameters of a Gaussian mixture noise model on the basis of said initial noise samples and determining (104) a weighted sum of respective refined parameters of the Gaussian mixture noise model and the corresponding initial parameters of the Gaussian mixture noise model.