UE Positioning via Collaborative Map Updates
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Solution Overview
Problem
In dense urban areas, vehicles and user equipment (UE) face challenges in determining accurate positioning information when network infrastructure equipment is absent or fails to provide reliable signals, as existing systems rely heavily on infrastructure like GPS, radar, and next-generation NodeB.
Innovation Solution
A positioning system that uses user equipment (UE) to transmit and receive positioning reference signals, allowing responders to share measurements and update map information without relying on network infrastructure, utilizing an initiator, responders, and an anchor device to classify objects as static or dynamic using machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network infrastructure equipment (GPS, radar, gNB) is used to determine positioning, then positioning information can be obtained through established systems, but accurate positioning cannot be achieved in dense urban areas where infrastructure is absent or signals are unreliable
Solution Approach 1:
The system enables user equipment to determine positioning autonomously using sensors (GPS, radar, lidar) and shared map data from other UEs, without requiring network infrastructure equipment. Each UE serves itself by collecting environmental data and participating in collaborative map building, resolving the contradiction by making the system independent of external infrastructure while maintaining reliability in dense urban areas.
Solution Approach 2:
The patent introduces shared map data as an intermediary between individual UEs and positioning determination. UEs contribute sensor measurements to build collaborative maps of static and dynamic objects, which then serve as reference information for positioning. This intermediary enables accurate positioning without direct reliance on network infrastructure, improving both reliability and environmental adaptability.
2Device complexity
If UE-based positioning systems operate without network elements, then system complexity is reduced and autonomy is improved, but measurement precision and positioning accuracy may deteriorate
Solution Approach 1:
The system merges measurements from multiple UEs to build collaborative maps of static and dynamic objects. By combining sensor data (GPS, radar, lidar) from multiple sources and using machine learning to classify objects, the system achieves high positioning accuracy without network elements. The merging of multiple independent measurement sources compensates for the lack of centralized infrastructure, maintaining precision while reducing complexity.
Solution Approach 2:
The patent creates virtual copies of environmental information through shared map data. Each UE contributes measurements that are processed into standardized map representations, which are then distributed to other UEs. These copied map information serve as reference data for positioning, enabling accurate determination without direct access to network infrastructure, thus maintaining measurement precision while simplifying the system.
Data Source
AI summary
In an aspect, an initiator transmits a positioning reference signal. The initiator receives a plurality of responder positioning reference signals sent from individual responders of a plurality of responders. The initiator transmits a measurement message comprising a first set of measurements that are determined based on the positioning reference signal and the plurality of responder positioning reference signals. The initiator receives responder measurement messages sent from the individual responders of the plurality of responders. The initiator receives updated map information from an anchor and updates pre-existing map information based on the updated map information.