Distributed Extended Kalman Filter for Jamming UAV Localization
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Solution Overview
Problem
Current methods for detecting and localizing jamming UAVs in wireless communication networks are inaccurate and computationally complex, relying on centralized processing and sensitive to node locations and noise power.
Innovation Solution
A distributed Extended Kalman Filter (DEKF) method that estimates the trajectory of a jamming UAV using a boundary node, processing jamming power locally without collaboration with other nodes, and an additional Distance-Ratio-Based DEKF (DEKF-DR) that reduces the number of boundary nodes required for localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centralized processing methods are used for jammer UAV localization, then measurement precision can be improved, but device complexity and computational requirements increase significantly
Solution Approach 1:
The patent divides the centralized localization problem into distributed sub-problems solved by individual boundary nodes. Each node independently estimates UAV trajectory using local measurements and an Extended Kalman Filter, eliminating the need for complex centralized processing while maintaining localization accuracy through distributed collaboration.
Solution Approach 2:
The patent transitions from traditional 2D localization to 3D trajectory estimation by incorporating vertical dimension measurements. The Extended Kalman Filter processes three-dimensional position data (x, y, z coordinates) to estimate UAV path, improving measurement precision by utilizing additional spatial information.
2Measurement precision
If more boundary nodes are deployed for localization, then measurement precision improves, but device complexity and resource requirements increase
Solution Approach 1:
The patent employs an Extended Kalman Filter that can achieve accurate trajectory estimation with partial measurements from fewer boundary nodes. The filter's predictive capability allows it to compensate for limited measurement data, reducing the number of nodes required while maintaining localization precision.
Solution Approach 2:
The Extended Kalman Filter implements feedback mechanisms where each boundary node continuously refines its trajectory estimates based on new measurements and previous state predictions. This iterative feedback process improves localization accuracy over time without requiring additional nodes.
3Productivity
If traditional Kalman filtering is used, then computational efficiency can be maintained, but measurement precision deteriorates in noisy environments
Solution Approach 1:
The patent extends the traditional Kalman Filter to handle non-linear measurements by incorporating Jacobian matrices for the measurement function. This Extended Kalman Filter transforms the non-linear measurement model into a linearized form, maintaining computational efficiency while improving trajectory estimation accuracy in noisy environments through optimized parameter processing.
Data Source
AI summary
Methods and apparatuses are provided for estimating a path of an aerial vehicle engaged in attacking network devices in a wireless communication network. A distance function corresponding to the aerial vehicle and a boundary node is determined based on an initial coordinate location of the aerial vehicle and an initial coordinate location of the boundary node. A function of jamming power received at the boundary node from the aerial vehicle is determined based at least on the first distance function and a transmission power of the boundary node. The function of jamming power represents a power associated with a jamming signal received from the aerial vehicle at the boundary node. A trajectory of the aerial vehicle at a plurality of time periods is estimated by the boundary node with an extended Kalman filter. The extended Kalman filter is determined based on the function of jamming power.