Multi-Carrier Phase Ranging Using Neural Network Band Gap Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing multi-carrier phase-based ranging techniques are hindered by band gaps within the measurement band, where some carriers are unusable due to interference or protocol restrictions, leading to degraded ranging performance.
Innovation Solution
A computer-implemented method that calculates squared channel responses for available carriers and uses a trained neural network to estimate these responses for unusable carriers in band gaps, thereby recovering the channel responses and improving distance estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multi-carrier phase-based ranging is performed using available carriers within a measurement band, then ranging functionality can be maintained, but ranging performance is significantly degraded due to band gaps caused by unusable carriers
Solution Approach 1:
The patent creates a virtual copy of the missing channel responses within band gaps by training a neural network to predict and generate these responses based on adjacent available carriers. This copied data restores the completeness of the measurement band without requiring additional physical measurements, thereby resolving the contradiction between maintaining functionality and preserving performance.
2Object-affected harmful factors
If band gaps with unusable carriers are present in the measurement band, then interference or protocol restrictions are avoided, but the ranging accuracy is significantly degraded
Solution Approach 1:
The patent converts the harmful effect of band gaps (which cause measurement incompleteness and accuracy degradation) into a beneficial solution by using neural network-based estimation to recover the missing information. The neural network learns the statistical relationships around band gaps and generates accurate estimates, effectively transforming the problem of unusable carriers into an opportunity to demonstrate advanced signal recovery capabilities.
3Measurement precision
If squared channel responses are estimated for unusable carriers using a trained neural network, then ranging accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by training the neural network offline before actual ranging operations. The network is pre-trained on synthetic channel data to learn the relationships between adjacent carriers and band gap regions. During runtime, the pre-trained network performs fast inference to estimate missing channel responses, avoiding the need for complex real-time computations and reducing operational computational complexity while maintaining high accuracy.
Data Source
AI summary
Examples relate to a method for performing multi-carrier phase-based ranging between two radios including: i) calculating squared channel responses based on channel measurements between the two radios for a plurality of available carriers within a measurement band for the phase-based ranging, where the measurement band has at least one band gap each with one or more unusable carriers for calculating squared channel responses, ii) estimating, via a trained neural network, squared channel responses for the unusable carriers in a respective band gap from squared channel responses of carriers adjacent to the respective band gap, and iii) estimating a distance between the two radios based on both the calculated and estimated squared channel responses.