Aerial UE State Estimation Using IMM and Neighbor Cell Positioning
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Solution Overview
Problem
Existing techniques for estimating the state of moving objects, such as drones, face challenges when applied to aerial UEs due to their unique movement patterns, leading to issues like increased interference and hazardous situations, particularly in LTE and 5G networks.
Innovation Solution
Implementing an interacting multiple-model (IMM) approach with extended Kalman filters to determine the movement state of aerial UEs by selecting neighbor cells based on specific criteria, performing positioning measurements, and using Hidden Markov Models to estimate the state, including initialization parameters and maneuver models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing state estimation techniques are applied to aerial UEs, then the network can track drone positions, but the estimation accuracy deteriorates due to unique aerial movement patterns
Solution Approach 1:
The patent applies interacting multiple-model (IMM) algorithms that dynamically switch between different movement models (constant velocity, constant acceleration, maneuvering models) to adapt to the changing movement patterns of aerial UEs. This dynamic approach resolves the contradiction by making the estimation system flexible enough to handle various aerial motion characteristics, thereby improving both accuracy and reliability simultaneously.
Solution Approach 2:
The patent changes the parameters of the estimation algorithm by introducing model-specific parameters (velocity, acceleration, maneuvering intensity) and dynamically adjusting their weights based on observed aerial UE behavior. This parameter adaptation enables the system to accurately track aerial UEs with diverse movement patterns, resolving the accuracy-reliability contradiction.
2Productivity
If aerial UEs are allowed to operate freely in the network, then network coverage and connectivity are improved, but interference increases and hazardous situations occur
Solution Approach 1:
The patent implements a feedback mechanism where the network continuously monitors the state (position, velocity, acceleration) of aerial UEs using IMM estimation and sends control commands back to the drones. This feedback loop enables the network to maintain broad connectivity while actively managing interference and preventing hazardous operations by adjusting aerial UE behavior in real-time.
Solution Approach 2:
The patent applies preliminary anti-action by predicting future states of aerial UEs using the IMM algorithm and proactively issuing control commands to prevent hazardous situations before they occur. The system anticipates potential interference or safety issues and takes preventive action, thereby maintaining network productivity while eliminating harmful effects.
3Measurement precision
If positioning measurements are performed in multiple neighbor cells, then the state estimation accuracy is improved, but the network complexity and measurement coordination increase
Solution Approach 1:
The patent segments the state estimation problem into multiple independent measurement components, each performed by different neighbor cells. Each cell independently measures positioning parameters and reports to the serving base station, which then integrates these segmented measurements using IMM algorithms. This segmentation reduces coordination complexity while maintaining high estimation accuracy.
Data Source
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AI summary
Embodiments include methods for estimating movement of an aerial user equipment (UE) by a first RAN node serving the aerial UE in a cell of the RAN. Such methods include determining initialization parameters for an interacting multiple-model (IMM) for movement of the aerial UE in the cell. The initialization parameters include a plurality of neighbor cells, in the RAN, in which positioning measurements should be performed for the aerial UE, and/or for at least one movement mode of the IMM, an initial state comprising a plurality of initial position estimates for the aerial UE. Such methods include determining a movement state for the aerial UE at a first time based on the initialization parameters and positioning measurements of the aerial UE that are performed in the cell and in at least a portion of the neighbor cells. Other embodiments include complementary methods for a second RAN node.