UE Movement State Estimation Using Multi-Site Doppler Measurements
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Solution Overview
Problem
Current LTE and NR positioning methods do not provide the required combination of accuracy and availability needed for use cases such as emergency location, personal safety, and unmanned aerial vehicles, particularly indoors.
Innovation Solution
A method for determining a user equipment (UE) movement state by performing Doppler shift and round trip time measurements from multiple transmission points, utilizing an interacting multiple-model (IMM) with Hidden Markov Model and extended Kalman filters to estimate two-dimensional position and velocity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current LTE and NR positioning methods are used, then positioning can be performed in the network, but the accuracy and availability are insufficient for indoor and aerial vehicle use cases
Solution Approach 1:
The patent changes the measurement parameters from traditional timing-based measurements to Doppler shift measurements. By measuring the Doppler shift of signals from multiple transmission points and using round trip time measurements, the system achieves higher positioning accuracy and availability for indoor and aerial vehicle scenarios where traditional methods fail.
Solution Approach 2:
The patent introduces an interacting multiple-model (IMM) estimator as an intermediary processing layer that combines Doppler shift measurements and round trip time measurements. This IMM estimator acts as a mediator that fuses multiple measurement types to produce accurate position and velocity estimates, resolving the contradiction between accuracy and availability.
2Measurement precision
If Doppler shift measurements from multiple transmission points are performed, then positioning accuracy is improved, but the measurement and calculation complexity increases
Solution Approach 1:
The patent employs dynamic modeling through the interacting multiple-model (IMM) estimator, which adapts to changing motion states of the user equipment. The system dynamically switches between different motion models (constant velocity, constant acceleration) based on the observed signal characteristics, making the complex measurement process manageable and adaptive to real-world conditions.
Solution Approach 2:
The patent segments the positioning problem into two distinct measurement components: Doppler shift measurements for velocity information and round trip time measurements for position information. This segmentation allows the system to process different types of measurements separately and combine them through the IMM estimator, reducing overall complexity while maintaining high 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
Enhances the accuracy and availability of UE positioning, particularly suitable for indoor environments and aerial vehicles, by providing precise 2D position and velocity estimation.
Implementation Method 1
performing positioning measurements on signals received from a plurality of transmission points (TPs) in the RAN. The positioning measurements include: first measurements of Doppler shift of signals from a first TP, and second measurements of Doppler shift of signals from a second TP
Data Source
AI summary
Embodiments include methods for determining a movement state of a user equipment (UE) operating in a radio access network (RAN). Such methods include performing positioning measurements on signals received from a plurality of transmission points (TPs) in the RAN, including first measurements of Doppler shift of signals from a first TP, second measurements of Doppler shift of signals from a second TP that is spatially separated from the first TP, and third measurements of signals from a third TP. The third TP can be the same as the first or second TP, or spatially separated from both. Such methods include determining a UE movement state based on the positioning measurements and an interacting multiple-model (IMM) that includes a first almost-constant velocity model, a second maneuver velocity model, and a Doppler shift bias state common to the first and second models. Other embodiments include complementary methods for a RAN node.