UAS Navigation Filter Weighting for GPS-Denied Flight
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Solution Overview
Problem
Unmanned aerial systems (UAS) face significant navigation challenges in GPS-denied environments due to errors introduced by noisy inertial navigation system measurements, leading to divergence from estimated trajectories and reduced survivability.
Innovation Solution
The implementation of an aircraft intent description language (AIDL) aid that identifies dynamic activity levels and affects aircraft states, allowing a navigation filter to adjust weighting schemes for INS measurements, thereby reducing error and improving trajectory estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dead reckoning navigation technique is used relying solely on INS measurements, then GPS availability is not required, but navigation accuracy deteriorates due to error propagation from noisy sensor measurements
Solution Approach 1:
The navigation filter dynamically adjusts the weighting scheme for INS measurements based on the current dynamic activity level of the aircraft. When high dynamic activity is detected, the system reduces the weight of INS measurements to minimize error propagation, while maintaining reliance on INS data for continuous navigation in GPS-denied environments.
Solution Approach 2:
The system changes the weighting parameter in the navigation filter based on the dynamic activity level determined by the AIDL aid. This parameter adjustment allows the system to optimize the trade-off between using INS data for continuous navigation and minimizing the impact of noisy measurements during high-dynamic maneuvers.
2Measurement precision
If dynamic activity level identification is implemented using AIDL aid, then trajectory estimation accuracy is improved, but device complexity increases due to additional navigation filter adjustments
Solution Approach 1:
The AIDL aid serves multiple functions: it identifies dynamic activity levels, determines affected aircraft states, and provides information for weighting scheme adjustment. This multi-functionality reduces the need for separate systems while improving trajectory estimation accuracy through intelligent use of existing INS data.
3Reliability
If weighting scheme adjustment is applied to INS measurements, then error propagation is reduced, but loss of information occurs by potentially discarding useful measurement data
Solution Approach 1:
The weighting scheme is dynamically adjusted based on real-time dynamic activity level identification rather than being fixed. This allows the system to maintain high weights for INS measurements during low-dynamic periods when data is reliable, while reducing weights only during high-dynamic maneuvers when error propagation is a concern, thus minimizing information loss.
Data Source
AI summary
Example navigation aids for increasing the accuracy of a navigation system are disclosed herein. An example method disclosed herein identifying, with an aircraft intent description language (AIDL) aid, an AIDL instruction as associated with a first dynamic activity level of a plurality of dynamic activity levels and determining, with the AIDL aid, an aircraft state to be affected by the AIDL instruction. The example method also includes changing, with a navigation filter, a weighting scheme for a measurement of the aircraft state obtained by an inertial navigation system (INS) of the aircraft and estimating, with the navigation filter, a trajectory of the aircraft based on the weighting scheme and the measurement.