Position Estimation Using Environmental Error Distributions
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Solution Overview
Problem
Current wireless positioning technologies face challenges in accurately determining the location of user equipment (UE) in noisy environments, such as indoor settings, due to high error variability in positioning measurements.
Innovation Solution
A method involving a positioning entity that obtains error distributions for various locations within an environment and compares these with positioning measurements from a UE to determine a location estimate, using uncertainty values to improve positioning performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional positioning methods are used in noisy environments, then positioning measurements can be obtained, but positioning accuracy deteriorates due to high error variability
Solution Approach 1:
The patent applies local quality by creating location-specific error distributions tailored to each geographic location. Instead of using a uniform error model, the system characterizes and stores error distributions specific to each location where positioning measurements are taken. This allows the positioning entity to select and apply the appropriate error distribution based on the UE's current location, thereby improving positioning accuracy in noisy environments by accounting for local environmental characteristics.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting the error distribution parameters based on the UE's location. The positioning entity obtains location information, identifies the corresponding error distribution for that location, and uses these location-specific parameters to evaluate positioning measurements. This approach transforms the static error modeling into a dynamic process where error characteristics change according to the geographic location, resolving the contradiction between maintaining reliable error estimation and achieving high positioning accuracy in varying environments.
2Measurement precision
If location-specific error distributions are implemented, then positioning accuracy improves, but system complexity increases due to storing and managing multiple error distributions
Solution Approach 1:
The patent applies preliminary action by pre-characterizing and storing error distributions for various locations before actual positioning operations. The system performs error distribution characterization in advance, creating a lookup table of location-specific error models that can be quickly retrieved during positioning. This preliminary preparation reduces the computational burden during real-time positioning, as the system only needs to identify the current location and retrieve the corresponding pre-computed error distribution rather than calculating error characteristics on-the-fly.
Solution Approach 2:
The patent introduces an intermediary component - the positioning entity - that acts as a mediator between the UE and the location management system. This positioning entity stores and manages the error distributions, receiving positioning measurements from the UE and comparing them against the appropriate location-specific error distributions. By centralizing the error distribution management in this intermediary component, the system reduces the complexity burden on individual UEs and base stations, allowing them to focus on their primary functions while the positioning entity handles the complex error characterization and evaluation.
Data Source
AI summary
Disclosed are techniques for positioning. In an aspect, a positioning entity obtains a set of error distributions associated with a set of locations associated with an environment, wherein each error distribution of the set of error distributions indicates an expected error distribution of positioning measurements associated with a corresponding location of the set of locations, obtains one or more positioning measurements obtained by a user equipment (UE) located within the environment; and determines a location estimate of the UE within the environment based on a comparison of uncertainty values associated with the one or more positioning measurements to the set of error distributions.