Aircraft Enhanced Vision System Latency Reduction
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Solution Overview
Problem
Enhanced Vision Systems (EVS) images used in aircraft display systems suffer from latency issues due to image enhancement processes, resulting in delayed video displays that are not current, which can hinder pilots' ability to recognize targets during approach and landing operations, especially in low visibility conditions.
Innovation Solution
A method and apparatus that utilize on-board GPS, inertial, and attitude sensors to enhance and adjust EVS images in real-time, ensuring they reflect the correct visual dimensions and position of the aircraft relative to the target, thereby eliminating latency and providing current visual feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image enhancement processing is applied to EVS images, then image quality and resolution are improved, but time delay increases making the displayed video not current
Solution Approach 1:
The system pre-calculates and stores enhancement parameters and lookup tables before actual image enhancement is needed. By preparing enhancement data in advance based on predicted aircraft position and terrain characteristics, the system avoids real-time computation delays while maintaining high image quality when images are displayed.
Solution Approach 2:
The system dynamically adjusts the level of enhancement processing based on aircraft altitude, approach phase, and criticality of the moment. During critical phases like final approach where time is paramount, minimal enhancement is applied. During less critical phases, more aggressive enhancement can be used, optimizing the balance between image quality and real-time performance.
2Measurement precision
If averaging or super-resolution approaches are used to improve EVS image quality, then resolution and feature definition increase, but significant processing delays are introduced
Solution Approach 1:
The system performs super-resolution calculations and stores enhanced image data in advance during periods when processing time is available. Pre-computed enhancement data is kept in memory or storage, allowing the system to display high-resolution images without real-time processing delays during critical approach and landing operations.
Solution Approach 2:
Instead of applying uniform super-resolution processing to entire images, the system identifies and enhances only critical regions such as the runway threshold, touchdown zone, and nearby obstacles. This selective enhancement approach maintains high resolution where needed while significantly reducing overall processing time and computational load.
3Loss of information
If enhanced vision system images are processed to improve clarity, then target recognition improves, but the images become outdated due to processing time
Solution Approach 1:
The system dynamically adapts the enhancement processing time and complexity based on aircraft approach phase and distance to runway. During early approach phases, more time can be allocated to enhancement. As the aircraft enters final approach and landing phases, the system reduces processing time and uses pre-computed data to ensure images remain current and reflect the latest aircraft position.
Solution Approach 2:
The system maintains continuous enhancement processing in the background during non-critical phases, constantly updating enhanced image data as new sensor data arrives. This continuous background processing ensures that when critical moments occur, current enhanced images are already available, eliminating the need for time-consuming real-time processing during critical operations.
Data Source
AI summary
A method for overcoming image latency issues of a synthetic vision system include generating (602, 704) a video comprising a plurality of images (300, 400, 500) of a target (208, 212) viewed from a moving platform (202), enhancing (604, 704) the resolution of the video, processing (606, 706) a parameter of the moving platform (202) related to the relative position of the platform to the target (208, 212), adjusting each of the plurality of images based on the processed parameter to simulate a real time video, and displaying (610, 710) the simulated real time video.