Wireless Positioning via Spatial Distribution Probabilities
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Solution Overview
Problem
Existing positioning techniques in wireless communication networks face challenges in accurately determining the location of user equipment, especially in indoor scenarios where GPS signals are weak, due to sensitivity to signal attenuation, reflection, and scattering, and do not effectively consider the probability of user presence at specific locations.
Innovation Solution
The method involves adjusting the estimated location of user equipment based on spatial distribution probabilities associated with various locations in the vicinity, using a network node with a processor that determines the location using traditional positioning techniques and incorporates spatial distribution probabilities to refine the estimate, thereby reducing the uncertainty area.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional positioning techniques (trilateration, triangulation, fingerprinting) are used to determine user location, then positioning can be performed using available signal data, but the results are highly sensitive to signal attenuation, reflection and scattering causing reduced accuracy
Solution Approach 1:
The patent changes the parameter space from direct signal strength comparison to probability distribution modeling. By representing user location as a probability distribution rather than a fixed point, the system can account for signal variations due to attenuation, reflection and scattering. The probability distribution parameters (mean position and covariance matrix) are updated iteratively to find the most likely user location despite signal degradation.
Solution Approach 2:
The patent introduces an intermediate probabilistic model between the raw signal measurements and the final position estimate. Instead of directly mapping signal strengths to positions, the system uses probability distributions as an intermediary representation that can handle the uncertainty and variability introduced by signal propagation effects in indoor environments.
2Measurement precision
If fingerprinting positioning is used to create a database of training signatures, then positioning can be performed by matching measured signals to database entries, but the uncertainty area remains large because it does not consider the probability of user presence at specific locations
Solution Approach 1:
The patent transforms the fingerprinting output from a discrete location estimate to a continuous probability distribution. By representing the user location as a probability distribution with mean and covariance, the system captures the uncertainty and likelihood of user presence at different locations, enabling more accurate position estimation and reducing the uncertainty area.
Solution Approach 2:
The patent adds a probabilistic dimension to the traditional fingerprinting approach. Instead of simply matching signal patterns to database entries, the system incorporates probability distributions that represent the likelihood of user presence, effectively adding a new dimension of information that improves location estimation accuracy and reduces uncertainty.
3Measurement precision
If the estimated location is adjusted based on spatial distribution probabilities, then the uncertainty area is reduced and location accuracy is enhanced, but the computational complexity and processing requirements increase
Solution Approach 1:
The patent applies partial action by iteratively updating only the necessary components of the probability distribution (mean position and covariance matrix) rather than recalculating the entire distribution from scratch. This reduces computational complexity while still achieving accurate location estimation by focusing on the most critical updates in each iteration.
Solution Approach 2:
The patent implements feedback through iterative updating of the probability distribution based on new signal measurements. The system continuously refines the location estimate by comparing predicted signal characteristics with actual measurements and adjusting the probability distribution accordingly, which improves accuracy while managing computational complexity through incremental updates.
Data Source
AI summary
Systems and methods are provided for a network node in a wireless communication network for generating an estimated location of a user equipment (UE). The method includes: receiving a request positioning message, determining the estimated location of the UE using a positioning technique, and adjusting the estimated location of the UE based on one or more spatial distribution probabilities associated with one or more locations in a vicinity of the estimated location.