Vehicle Dynamic Model Navigation Filter for UAVs
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Solution Overview
Problem
Current navigation systems for unmanned aerial vehicles (UAVs) face challenges during Global Navigation Satellite System (GNSS) outages, as inertial navigation systems (INS) suffer from low long-term accuracy and high drift, leading to navigation uncertainty and potential instability, especially in non-line-of-sight flights.
Innovation Solution
Implementing a vehicle dynamic model (VDM) as the main process model within the navigation filter, which uses IMU and GNSS data, and allows for autonomous navigation without additional sensors, thereby improving accuracy and reliability by rejecting physically impossible movements suggested by IMU errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If INS is used for navigation during GNSS outage, then autonomous navigation is maintained, but navigation accuracy deteriorates rapidly due to drift
Solution Approach 1:
The patent introduces a vehicle dynamic model (VDM) as an intermediary between the INS and the navigation filter. The VDM uses control inputs and physical constraints to generate predicted vehicle states that serve as a reference for correcting INS drift, thereby maintaining navigation accuracy during GNSS outages without sacrificing autonomous operation
Solution Approach 2:
The patent implements a feedback mechanism where the navigation filter continuously compares INS output with VDM predictions and adjusts the navigation solution accordingly. This feedback loop corrects accumulated INS errors by referencing physically plausible states derived from control inputs and dynamic models, preventing drift while maintaining autonomy
2Measurement precision
If additional navigation sensors are employed to aid INS during GNSS outage, then navigation accuracy is improved, but system cost and complexity increase
Solution Approach 1:
The patent enables the existing INS system to self-correct its drift errors by utilizing its own control inputs and a mathematical dynamic model. The VDM serves as a virtual sensor that generates predicted states based on physical laws and control commands, allowing the system to maintain accuracy without adding external hardware sensors
Solution Approach 2:
The patent replaces the need for additional physical navigation sensors with a mathematical model-based approach. Instead of adding more hardware sensors to measure vehicle states, the system uses a computational dynamic model that simulates vehicle behavior based on control inputs and physical principles, substituting mechanical sensing with virtual sensing
3Measurement precision
If vision based methods are used to aid navigation, then relative or absolute measurements are provided, but system weight and hardware complexity increase
Solution Approach 1:
The patent replaces vision-based optical sensing with a mathematical dynamic model approach. Instead of using cameras and vision processing algorithms to measure vehicle states, the system uses a computational model that predicts vehicle behavior based on control inputs and physical laws, substituting optical sensing with model-based virtual sensing
Data Source
AI summary
A navigation system including a vehicle dynamic model (VDM) that serves as the main process model within a navigation filter is described. When used in an unmanned aerial vehicle (UAV), the navigation system may work in communication with inertial measurement units (IMUs) and environment dependent sensors such as GNSS receivers. Particularly, the navigation system is beneficial in the case of GNSS signal reception outages, where conventional IMU coasting drifts quickly. Yet, the navigation system may also be employed in other scenarios, for example during GNSS presence for improved positioning, velocity and attitude determination, or in combination with GNSS when no IMU is available by design or due to a failure. In the navigation system, a solution to VDM equations provides an estimate of position, velocity, and attitude, which can be updated within a navigation filter based on available observations, such as IMU data or GNSS measurements.