Drone Type State Estimation via Recursive Kinematic Updates
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Solution Overview
Problem
The increasing use of LTE and NR capable drones, particularly rogue drones, poses interference and safety risks due to their ability to fly beyond the range of radio control equipment, causing interference and hazardous situations in restricted airspace, necessitating a method to identify and locate these drones within wireless communication systems.
Innovation Solution
A method for type state estimation of user equipment (UE) in a wireless communication system, which recursively updates a probability estimate of a UE being a drone based on kinematic state updates, allowing for assignment as a drone when the estimate exceeds a threshold, utilizing kinematic state estimates and Bayesian processing to discriminate between airborne and terrestrial UEs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If drones are allowed to fly freely using cellular networks, then their operational range is extended, but interference problems and safety risks increase
Solution Approach 1:
The system performs preliminary identification of drones before they can cause interference or safety issues. By continuously monitoring kinematic state estimates and updating type state probabilities, the network identifies drones in advance, allowing preventive measures to be taken before harmful effects occur.
Solution Approach 2:
The patent introduces an intermediary identification system that mediates between drone operations and network interference. The type state estimation mechanism acts as a mediator that classifies UEs as drone or non-drone types, enabling the network to apply different handling rules to different UE types, thus resolving the conflict between extended range and interference prevention.
2Productivity
If rogue drones are not identified, then network operations continue uninterrupted, but interference and hazardous situations increase
Solution Approach 1:
The system implements continuous feedback through recursive updating of type state estimates based on kinematic state estimates. This feedback mechanism allows the system to continuously monitor UE behavior and adjust identification decisions in real-time, maintaining network operation continuity while detecting and responding to rogue drones as they exhibit characteristic flight patterns.
Solution Approach 2:
The patent replaces traditional mechanical or manual drone detection methods with a signal-processing-based identification system. By substituting physical detection mechanisms with computational analysis of kinematic state estimates and type state probabilities, the system achieves continuous, automated identification without interrupting normal network operations.
3Measurement precision
If type state estimation is performed recursively with kinematic state updates, then drone identification accuracy is improved, but computational complexity increases
Solution Approach 1:
The system applies partial action by updating type state estimates only when kinematic state estimates provide new information. Rather than performing exhaustive computations at every time step, the recursive updating mechanism selectively processes relevant kinematic data, achieving high identification accuracy while reducing unnecessary computational overhead.
Data Source
AI summary
A method for type state estimation of a user equipment connected to a wireless communication network. The method comprises updating, recursively, of a type state estimate. The type state estimate is a probability for the user equipment to be a drone conditioned on obtained kinematic state estimate updates concerning the user equipment. The user equipment is assigned to be a drone as a response on the type state estimate exceeding a threshold.