Aircraft Heading Estimation Using Incremental Feature Matching
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Solution Overview
Problem
Aircraft struggle to accurately estimate heading in low visibility conditions due to insufficient detection of approach lighting system (ALS) lights or objects, leading to increased risk of heading errors during landing.
Innovation Solution
An aircraft heading estimation system using sensors to detect features, compare with expected patterns, and incrementally update heading estimates as resolution improves, utilizing long-range sensors and an image processor to generate and refine heading estimates based on expected features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high resolution sensors are used to detect approach lighting system lights, then heading estimation accuracy is improved, but the sensors cannot penetrate the atmosphere and range is limited
Solution Approach 1:
The patent combines multiple sensor types (long-range sensors like radar and high-resolution sensors like cameras) into a fused sensing system. The long-range sensors detect features at distance while the high-resolution sensors provide detailed imaging, and the data is merged through processing to achieve both long range and high precision heading estimation simultaneously.
Solution Approach 2:
The patent introduces an intermediary processing system that includes feature detection modules, matching modules, and heading calculation modules. This intermediary processing layer bridges the gap between raw sensor data from long-range sensors and the requirements for high-precision heading estimation by performing intermediate feature extraction and registration operations.
2Length of stationary object
If long-range sensors are used to detect features, then detection range is improved, but resolution is insufficient for accurate heading estimation
Solution Approach 1:
The patent merges long-range sensor data with high-resolution sensor data, allowing the system to benefit from both extended detection range and sufficient resolution for accurate heading estimation. The fusion process combines the complementary strengths of both sensor types.
Solution Approach 2:
The patent performs preliminary feature detection and extraction from long-range sensor data before final heading calculation. By pre-processing and identifying features in advance from the long-range sensor input, the system can then refine these features using high-resolution sensor data, achieving both range and precision.
3Reliability
If multiple verification procedures are implemented for heading estimation, then reliability is improved, but system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors the consistency between heading estimates from different sensors and verification procedures. The feedback loop adjusts and refines the estimation process, improving reliability while the automated feedback reduces the need for manual verification procedures.
Solution Approach 2:
The system performs self-verification through automated comparison of heading estimates from multiple sensor sources. The processing system automatically checks for consistency and resolves discrepancies without requiring external manual verification, thereby improving reliability while reducing operational complexity.
Data Source
AI summary
An aircraft heading estimation system includes: one or more sensors arranged on the aircraft to detect features in a viewing region and provide feature signals indicative of the detected features; an image processor storing predetermined image information of one or more expected features, being features that might be expected to be present in the viewing region, the image processor configured to compare the features indicated by the feature signals with the one or more expected features to generate an estimated observed image and to derive an estimated heading for the aircraft based on the estimated observed image; where the image processor is further configured to incrementally update the estimated observed image with new feature signals generated by the sensors as their distance to the viewing region changes, and to incrementally updated the estimated heading based on the updated estimate observed image.