3D Position Estimation Using Signal Signature Matrices
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Solution Overview
Problem
Existing mobile communication systems face challenges in accurately estimating the vertical position of user devices, particularly in environments where traditional sensors like barometric sensors are limited by errors and lack of accuracy.
Innovation Solution
The system generates signal signature matrices based on reference signals received from user devices at multiple nodes, which are then used to train a machine learning model. This model, when applied to the signal signature matrices, provides a three-dimensional position estimate, including a refined vertical position, without relying on external sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional barometric sensors are used for vertical position estimation, then the system is simple to implement, but the measurement precision and reliability are insufficient
Solution Approach 1:
The patent replaces traditional mechanical/barometric sensors with a signal processing-based approach using machine learning models that analyze reference signals from multiple communication nodes to estimate vertical position, thereby improving measurement precision without relying on error-prone physical sensors
Solution Approach 2:
The patent introduces signal signature matrices as an intermediary representation that captures channel characteristics from multiple nodes, which then feed into the machine learning model for position estimation, enabling accurate 3D positioning without direct sensor measurement
2Measurement precision
If signal signature matrices from multiple nodes are processed through machine learning models, then the position estimation accuracy is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent pre-processes reference signals from multiple nodes to generate signal signature matrices before position estimation, organizing the data in advance to facilitate efficient machine learning model processing and reduce real-time computational burden
Solution Approach 2:
The patent transforms raw reference signals into signal signature matrices by extracting specific channel parameters and characteristics, changing the data representation to a form that is more suitable for machine learning processing and improves estimation accuracy
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution achieves accurate three-dimensional position estimation, including vertical positioning, with improved reliability and reduced errors compared to traditional methods, enhancing emergency response capabilities and overall system accuracy.
Implementation Method 1
using cross-correlation to isolate reference signals received from individual user devices at each communication node
Data Source
AI summary
A method, apparatus and computer program is described comprising: obtaining reference signals received at a plurality of nodes of a communication system from a user device; generating signal signature matrices based on real and imaginary components of the obtained reference signals; and generating a first three-dimensional position estimate for the user device by applying signals based on the generated signal signature matrices to an input of a model.