Vision-Based Landing Assurance for Touchdown Zone Go/No-Go
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Solution Overview
Problem
Autonomous flight operations face challenges in achieving high integrity automated landings without relying on Category III Instrument Landing Systems (ILS), due to limitations and vulnerabilities in Global Navigation Satellite Systems (GNSS) such as interference from spoofing and jamming.
Innovation Solution
A system that uses at least one processor to obtain flight path information and image sensor data from an aircraft, identifies runway features, determines a touchdown zone, and compares the flight path vector (FPV) or flight path predictor (FPP) with the touchdown zone to make go/no-go decisions for landing or go-around procedures independently of ILS or GNSS guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GNSS systems are used for navigation and landing guidance, then global coverage and basic navigation capability are improved, but vulnerability to spoofing and jamming interference worsens
Solution Approach 1:
The patent introduces an intermediary vision-based runway detection system that mediates between the aircraft and the landing guidance process. Instead of relying directly on vulnerable GNSS signals, the system uses image sensors to capture visual data of the runway environment, processes this data to determine runway orientation and position, and uses this information as a mediator to guide the landing process independently of GNSS
Solution Approach 2:
The patent replaces the electronic signal-based GNSS system with a vision-based optical system for runway detection and alignment. Image sensors capture visual information, which is then processed to determine runway parameters, substituting the mechanical/electronic GNSS signal reception with an optical vision-based approach that is immune to radio frequency interference
2Measurement precision
If Category III ILS systems are used for automated landing, then precision and reliability of landing guidance are improved, but system complexity and dependency on specialized infrastructure worsen
Solution Approach 1:
The patent extracts the essential function of runway detection and alignment from the complex ILS system. Instead of using the full Category III ILS infrastructure with its multiple components (localizer, glide slope, marker beacons), the system extracts only the necessary visual detection and alignment functions, implementing them through a simplified vision-based system that achieves similar precision without the infrastructure complexity
Solution Approach 2:
The patent creates a visual copy or representation of the runway environment through image capture and processing. By creating a digital model of the runway orientation and position from visual data, the system replicates the guidance function of ILS using optical information, achieving comparable precision with reduced system complexity
3Productivity
If fully automated landing operations are implemented without onboard pilot monitoring, then operational efficiency and productivity are improved, but safety and decision-making capability worsen due to inability to handle unexpected situations
Solution Approach 1:
The patent implements continuous feedback loops where image sensors constantly monitor the runway environment, the system processes this visual information to determine alignment status, and adjusts the landing guidance accordingly. This real-time feedback mechanism enables fully automated operations to maintain safety by continuously verifying conditions and making adaptive decisions without pilot intervention
Solution Approach 2:
The patent performs preliminary detection and analysis of runway conditions and alignment status before the critical landing phase. By using image sensors to pre-assess the environment and calculate alignment parameters in advance, the system prepares safety-critical information beforehand, enabling automated decision-making to proceed with high reliability during the actual landing execution
Data Source
Figure 1A
Figure 1B
Figure 2A
AI summary
A system may include at least one processor (404) configured to: obtain data of an aircraft (302), the data including and/or associated a flight path vector (FPV) and/or a flight path predictor (FPP); obtain image sensor data output; identify features associated with a runway (103); determine that the identified features are indicative of the runway; determine a touch down zone on the runway; determine whether the FPV and/or the FPP is in the touch down zone when the aircraft is at a decision altitude and/or is predicted to be in the touch down zone when the aircraft will be at the decision altitude; and (i) output a notification to proceed with a landing procedure, (ii) perform an operation configured to cause the aircraft to proceed with the landing procedure, (iii) output a notification to perform a go-around procedure, and/or (iv) perform an operation configured to cause the aircraft to perform the go-around procedure.