UAV Position Estimation Using Flight Parameters and External Data Correction
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Solution Overview
Problem
Existing methods for determining the state and position of unmanned aerial vehicles (UAVs) are not robust against data traffic restrictions or impairments, leading to potential inaccuracies and increased costs for locating UAVs.
Innovation Solution
A method involving repeated estimation of the UAV's current position using known positions and flight parameters, storage of estimates in a database, and correction based on externally recorded status data, including ADS-B and air traffic control data, to ensure accurate positioning even with impaired data connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the aircraft's position is determined by continuous data communication with ground stations, then position accuracy is improved, but the system becomes vulnerable to data traffic restrictions and impairments
Solution Approach 1:
The system performs preliminary actions by repeatedly estimating the aircraft's position based on previously known positions and flight parameters, storing these estimates in a database before actual position determination is needed. This allows the system to have pre-computed position data ready, reducing dependency on continuous real-time data communication and maintaining functionality even when data traffic is restricted or impaired.
Solution Approach 2:
The system introduces an intermediary estimation mechanism that acts as a mediator between the aircraft and ground stations. Instead of relying directly on continuous data communication, the system uses flight parameters and previously known positions as intermediaries to compute and store estimated positions, thereby reducing direct dependency on vulnerable data channels while maintaining position determination capability.
2Reliability
If the aircraft's position is estimated using flight parameters and previously known positions, then robustness against data traffic impairments is improved, but position accuracy may deteriorate without external validation
Solution Approach 1:
The system implements feedback by receiving externally acquired state data (such as ADS-B data or radar data from air traffic control) that indicates the aircraft's actual position, comparing this external data with the stored estimated positions, and using the determined deviation to correct the estimates in the database. This closed-loop feedback mechanism ensures that while the system maintains robustness through estimation, it also maintains or improves accuracy through periodic external validation and correction.
3Measurement precision
If external data sources like ADS-B and radar are used to validate position estimates, then position accuracy is improved, but system complexity increases
Solution Approach 1:
The system applies universality by designing a multi-functional position determination system that can operate in multiple modes: it can estimate positions using flight parameters when external data is unavailable, and it can validate and correct these estimates using external data sources like ADS-B, radar, or air traffic control data when available. This universal approach allows the same system to maintain robustness across different operational conditions without requiring separate specialized systems for each data source.
Data Source
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AI summary
A method (2) for determining the state of an unmanned aircraft comprises the steps of repeatedly estimating (6) a current position of the aircraft based on at least one previously known position (8) and at least one flight parameter, and storing (10) the respective estimated current position in a database, receiving (12) externally acquired state data that show a position at a determination time, determining (20) an estimate of a position of the aircraft at the determination time based on the stored positions in the database, comparing (22) the estimated position of the aircraft at the determination time and the recorded position at the determination time and determining (25) a deviation of the estimated position from the recorded position, and correcting (28) the stored positions in the database that follow the determination time, based on the deviation.