EFVS Radar Runway Detection for Pilot Monitoring
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Solution Overview
Problem
Current Enhanced Flight Vision Systems (EFVS) have limited range and accuracy in low visibility conditions, such as heavy fog, and do not provide sufficient context for pilots monitoring (PM) to verify the reliability and accuracy of the system during approach and landing procedures.
Innovation Solution
An EFVS with a Doppler weather radar system that enhances reflectivity of radar returns from runway structures, generating three-dimensional models and two-dimensional images, which are then displayed on a Head Down Display (HDD) to provide the PM with situational context, including a 2-D electronic moving map with radar overlays, allowing for better verification of system reliability and pilot actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If passive sensors (FLIR camera or visible light camera) are used for EFVS, then higher quality video imagery is provided, but the system is unable to identify required visual references in certain low visibility conditions such as heavy fog
Solution Approach 1:
The patent combines passive sensors (FLIR or visible light cameras) with active millimeter wavelength radar systems to create a hybrid EFVS. The passive sensors provide high-quality imagery under normal conditions, while the radar system activates in low visibility conditions to detect visual references that passive sensors cannot see, thereby merging the strengths of both sensing approaches.
Solution Approach 2:
The millimeter wavelength radar system acts as an intermediary that bridges the gap between passive sensing capabilities and the need for visual reference identification in heavy fog. The radar penetrates fog to detect references, and its data is integrated with passive sensor imagery to provide continuous reliable visualization.
2Reliability
If active millimeter wavelength radar systems are used for EFVS, then better identification of required visual references is provided in low visibility conditions, but the quality of the imagery is not as good
Solution Approach 1:
The system merges radar-generated 3-D models rendered as 2-D images with passive sensor video imagery. The radar provides reliable visual reference identification in fog, while the passive sensors maintain high imagery quality. The combined display shows radar-detected references overlaid on or integrated with high-quality video, achieving both goals simultaneously.
Solution Approach 2:
Different regions of the display have different quality characteristics: areas detected by radar show reliable reference identification with appropriate visual indicators, while areas detected by passive sensors show high-quality natural imagery. The system applies local quality optimization based on the detection capabilities of each sensor type in different spatial zones.
3Ease of operation
If EFVS imagery is presented to the PM on a head down display (HDD), then the PM can view the imagery, but the PM must look away from the cockpit windscreen and is unable to see the external surroundings of the aircraft
Solution Approach 1:
The system adds contextual information in additional dimensions on the HDD display. It shows multiple views including front view, top view, and side view of the aircraft and surrounding environment. This multi-dimensional presentation provides spatial context without requiring the PM to physically reposition their head, maintaining situational awareness while enabling verification.
Solution Approach 2:
The HDD displays replicated views of the external environment captured by sensors, including the cockpit windscreen view and surrounding airport environment. These copied visual representations provide contextual information about external surroundings, allowing the PM to verify EFVS accuracy without direct visual contact with the actual environment.
4Reliability
If the EFVS provides imagery without context of external surroundings, then the PM can verify EFVS information, but it is very difficult for the PM to detect problems in the EFVS imagery beyond gross failures
Solution Approach 1:
The system provides feedback to the PM by displaying multiple contextual views including the actual external environment captured by sensors, the EFVS processed imagery, and comparative analyses. This feedback loop enables the PM to detect subtle discrepancies between raw sensor data and processed EFVS output, improving problem detection beyond gross failures.
Solution Approach 2:
The system presents EFVS verification information in additional dimensions by showing top-down views, side views, and multi-angle perspectives alongside the primary EFVS imagery. This multi-dimensional presentation reveals spatial relationships and potential errors that would be invisible in a single-view display, enhancing the PM's ability to detect subtle problems.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The EFVS with Doppler weather radar provides improved range and accuracy in low visibility conditions, enabling PMs to effectively verify the system's reliability and pilot actions, reducing the risk of errors during approach and landing.
Implementation Method 1
An EFVS with a Doppler weather radar system that enhances reflectivity of radar returns from runway structures
Implementation Method 2
Doppler weather radar system that enhances reflectivity of radar returns
Data Source
AI summary
An image processing system for enhanced flight vision includes a processor and memory coupled to the processor. The memory contains program instructions that, when executed, cause the processor to receive radar returns data for a runway structure, generate a three-dimensional model representative of the runway structure based on the radar returns data, generate a two-dimensional image of the runway structure from the three-dimensional model, and generate an aircraft situation display image representative of the position of the runway structure with respect to an aircraft based on the two-dimensional image.