Indoor Wireless Positioning Using Positional Feature Distributions
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Solution Overview
Problem
Conventional indoor positioning methods suffer from poor precision in complex environments due to factors like multipath and non-line-of-sight scenarios, limiting their effectiveness in scenarios with obstacles and background noise.
Innovation Solution
A method and device for wireless communication that utilize distributions of positional features between a user node and multiple signal source nodes to determine the user's position, employing a model that infers positional and environmental information from received signals using deep learning networks to enhance positioning accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional positioning algorithms (tag positioning, triangulation, fingerprint) are used, then the positioning system can operate in indoor environments, but positioning precision deteriorates in complex environments with multipath, background noise, and non-line-of-sight scenarios
Solution Approach 1:
The patent changes the parameter representation from single-valued estimates to probability distributions of positional features. Instead of using a single distance or angle value, the system uses distribution parameters (mean, variance, skewness, kurtosis) to characterize the positional features, allowing the system to model uncertainty and environmental effects explicitly. This parameter transformation enables the system to handle multipath and non-line-of-sight scenarios by representing positional information as distributions rather than deterministic values.
Solution Approach 2:
The patent adds a probabilistic dimension to the positioning problem by transforming single-valued positional features into distribution parameters. This dimensional expansion from point estimates to distribution parameters allows the system to capture environmental complexity and uncertainty, improving robustness in complex indoor scenarios without requiring additional physical sensors or infrastructure.
2Measurement precision
If single-valued positional feature estimation is used, then the positioning calculation is simple, but positioning precision and global robustness are limited
Solution Approach 1:
The patent transforms the calculation complexity by changing from single-valued arithmetic operations to distribution parameter computations. While the mathematical operations become more sophisticated (involving moments and probability distributions), the structured approach using distribution parameters provides a systematic framework that manages complexity through mathematical organization rather than computational brute force.
Data Source
AI summary
A wireless communication method and device are provided. The method includes that: the distribution of positional features between a user node and at least three signal source nodes is acquired; and the position of the user node is determined according to the distribution of the positional features between the user node and the at least three signal source nodes.