Aircraft Landing Navigation Using Video-INS Kalman Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current aircraft navigation systems during the landing phase face challenges in precision and cost due to the temporal degradation of inertial navigation systems, which can result in significant errors depending on the INS system quality.
Innovation Solution
Integrating video data from a digital camera on the aircraft into an extended Kalman filter algorithm, which uses both satellite navigation and inertial data to compute navigation parameters, improving precision and reducing the reliance on high-cost INS systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a high-quality INS system is used to maintain navigation precision during landing, then measurement precision is improved, but device cost increases
Solution Approach 1:
The patent combines video data from cameras with INS and GNSS data in an extended Kalman filter. The video data provides visual constraints on aircraft position and orientation relative to the runway, merging multiple data sources to achieve high navigation precision without requiring a high-cost INS system alone.
Solution Approach 2:
The video data acts as an intermediary measurement source that bridges the gap between low-cost INS systems and high-precision navigation requirements. By processing video data through the extended Kalman filter, the system obtains precise navigation parameters without directly using expensive high-precision INS hardware.
2Measurement precision
If INS drift is reduced to maintain precision over time, then measurement precision is improved, but device cost increases
Solution Approach 1:
The extended Kalman filter continuously processes video data to provide feedback corrections to the INS navigation parameters. This feedback mechanism compensates for INS drift over time by comparing video-based position estimates with INS predictions, maintaining precision without requiring a high-quality INS system.
Solution Approach 2:
The navigation system uses a composite approach by combining multiple data sources (video data, INS data, GNSS data) with different characteristics. This composite measurement model leverages the strengths of each source to achieve reliable precision over time without depending on a single high-cost INS system.
3Measurement precision
If video data integration is added to the navigation system, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The extended Kalman filter is enhanced to handle multiple data types (video, INS, GNSS) within a single unified processing framework. This multi-functional approach integrates diverse measurement sources without requiring separate processing systems, managing complexity while improving precision.
4Device complexity
If lower-cost INS systems are used, then device cost is reduced, but measurement precision deteriorates
Solution Approach 1:
The system uses video data to create a visual copy or representation of the aircraft's position and orientation relative to the runway. This video-based measurement serves as a substitute for high-precision INS measurements, allowing lower-cost INS systems to be used while maintaining navigation precision through the extended Kalman filter integration.
Data Source
AI summary
A device for determining navigation parameters of an aircraft during a landing phase includes a video system including at least one digital video camera arranged on the aircraft, the digital video camera being configured to generate on the aircraft current video data relating to at least one characteristic point on the Earth, whose coordinates are known, and a data processing unit including an extended Kalman filter and configured to determine the navigation parameters on the basis of current navigation data of the aircraft, arising from a satellite navigation system, current inertial data of the aircraft, as well as said video data.