Drone Self-Localization Using RIS Fusion in Urban Multipath
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Solution Overview
Problem
Current drone self-localization systems rely solely on GPS signals, which are prone to inaccuracies due to signal interference and multipath effects in urban environments.
Innovation Solution
A system and method utilizing reconfigurable intelligent surfaces (RIS) and data fusion algorithms, specifically an extended Kalman filter, to combine RIS signals with GPS and other positioning signals for enhanced self-localization of drones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS signals are used for drone self-localization, then the system is simple and easy to operate, but the measurement precision deteriorates due to signal interference and multipath effects in urban environments
Solution Approach 1:
The patent combines GPS signals with RIS (Reconfigurable Intelligent Surface) signals to create a hybrid positioning system. The fusion algorithm integrates measurements from both GPS satellites and RIS elements, leveraging the complementary strengths of each system to achieve higher location accuracy than GPS alone, especially in urban environments with multipath effects
Solution Approach 2:
The patent introduces RIS as an intermediary component that reflects and redirects signals between drones and base stations. The RIS elements act as intermediate reflectors that create additional signal paths, enabling more accurate time-of-flight measurements and location determination through the fusion of direct and reflected signal paths
2Reliability
If GPS signals are used for drone self-localization, then the device complexity is low, but the reliability deteriorates due to signal interference and multipath effects
Solution Approach 1:
The patent merges GPS positioning with RIS-based positioning to create a redundant measurement system. By fusing data from multiple independent signal sources (GPS satellites and RIS elements), the system achieves higher reliability because the failure or degradation of one signal source can be compensated by the other
Solution Approach 2:
The fusion algorithm continuously processes and compares measurements from GPS and RIS signals, adjusting the weighting and trust in each signal source based on their respective quality indicators. This feedback mechanism allows the system to dynamically adapt to changing signal conditions and maintain high reliability
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 integration of RIS signals with GPS signals improves the accuracy and reliability of drone self-localization, particularly in challenging environments, by mitigating multipath effects and enhancing signal strength.
Implementation Method 1
the received RIS signals as passive signals transmitted from the RIS transceiver and reflected from the RIS device
Data Source
AI summary
A controller for controlling an autonomous vehicle. The controller comprises a reconfigurable intelligent surface (RIS) transceiver configured to receive RIS signals from a RIS device, a positioning signal receiver configured to receive positioning signals from a positioning signal transmitter, and a processor. The processor is configured to process the RIS signals to produce RIS data, process the positioning signals to produce positioning data, fuse the RIS data and the positioning data using a data fusion algorithm to compute a location of the autonomous vehicle, and control operation of the autonomous vehicle based on the computed location.