Kinematic State Estimation Using Interacting Multiple Model Filtering
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Solution Overview
Problem
Current technologies lack accurate methods for kinematic state estimation of user equipment (UE) connected to wireless communication networks, particularly for drones, which is crucial for identifying and mitigating rogue drones causing interference and safety hazards.
Innovation Solution
A method and node architecture for kinematic state estimation using interacting-multiple-model filtering with three interacting models (constant velocity, constant acceleration, and constant position Wiener processes) based on range rate and range measurement data, enabling accurate tracking of UE movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If interacting-multiple-model filtering with three interacting models is used for kinematic state estimation, then measurement precision and estimation accuracy are improved, but device complexity increases
Solution Approach 1:
The kinematic state estimation is divided into three separate Wiener process models (constant velocity, constant acceleration, and constant position), each handling a specific motion regime. This segmentation allows the complex estimation problem to be broken down into manageable components that can be processed independently and then combined through the IMM framework.
Solution Approach 2:
The system dynamically switches between different motion models based on the current kinematic state and measurement characteristics. The interacting multiple-model filtering algorithm continuously adjusts which model is active and how much weight to assign to each model, enabling the system to adapt to changing flight conditions without requiring a completely complex monolithic algorithm.
2Reliability
If range rate and range measurement data are processed through interacting-multiple-model filtering, then reliability of rogue drone identification is improved, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary model selection and weight assignment based on the current measurement data characteristics before executing the full kinematic state estimation. By pre-determining which Wiener process models are most relevant for the current situation, the system reduces unnecessary computations and focuses processing resources on the most critical estimation tasks.
Solution Approach 2:
The interacting multiple-model filtering algorithm continuously uses feedback from measurement data to adjust the probability weights of different motion models. This feedback mechanism allows the system to converge quickly on the correct kinematic state by repeatedly testing and refining model predictions against actual measurements, reducing the overall processing time required to achieve reliable identification.
Data Source
AI summary
A method for kinematic state estimation of a user equipment connected to a wireless communication network includes obtaining range rate measurement data defining a change rate of a distance between the user equipment and a range rate measuring position and obtaining range measurement data defining a distance between the user equipment and a range measuring position. A kinematic state estimation of the user equipment is performed based on at least the range rate measurement data and the range measurement data. The kinematic state estimation includes interacting-multiple-model filtering using three interacting models. The interacting-multiple-model filtering includes a three-dimensional constant velocity movement Wiener process, a three-dimensional constant acceleration movement Wiener process, and a three-dimensional constant position Wiener process.