Aircraft Image Sensor Geolocation Using Opportunity Landmarks
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Solution Overview
Problem
The precision of an aircraft's image sensor location and orientation is often inadequate due to inertial drift and the lack of precise GPS, making conventional recalibration methods, such as using landmarks, cumbersome and unreliable, especially in environments without suitable landmarks.
Innovation Solution
A method is developed to create 'opportunity landmarks' in-flight using a geo-referenced terrain database, allowing for precise location and orientation of the image sensor by identifying and tracking notable ground features, and using a Kalman filter for accurate estimation based on distance and orientation measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional recalibration methods using landmarks are employed, then the precision of sensor location and orientation can be improved, but the complexity of mission preparation and operation increases significantly
Solution Approach 1:
The system automatically creates opportunity landmarks using the terrain database and current sensor position without requiring external preparation or selection of landmarks by operators. The sensor platform itself generates the calibration references it needs, eliminating the need for pre-planned landmark missions.
Solution Approach 2:
The system changes the parameter of landmark availability from fixed and limited (conventional landmarks) to dynamic and abundant (any terrain feature from database). By transforming the terrain database into usable opportunity landmarks, the system expands the available calibration targets from rare pre-selected features to any ground feature that can be identified in the terrain data.
2Measurement precision
If conventional recalibration methods using landmarks are employed, then the precision of sensor location and orientation can be improved, but the time required for landmark search and reconnaissance increases
Solution Approach 1:
The terrain database containing potential landmark information is prepared and stored in advance. During flight, the system only needs to query this pre-prepared database based on current position, eliminating the need for time-consuming real-time landmark search and reconnaissance that would otherwise be required.
Solution Approach 2:
The system automatically queries the terrain database using its current approximate position to generate opportunity landmarks, eliminating the need for manual landmark search and reconnaissance by operators. The recalibration process becomes self-service rather than requiring time-intensive human intervention.
3Speed
If GPS and inertial units are used for position determination, then continuous position data is available, but the precision deteriorates due to inertial drift
Solution Approach 1:
The system uses the identified opportunity landmark from the terrain database as a feedback reference to correct the drift-affected inertial position. By comparing the inertially-derived position with the known precise position of the opportunity landmark, the system can detect and correct position errors, maintaining both continuous updates and high accuracy.
Solution Approach 2:
The system creates an asymmetric correction approach where the high-precision terrain database position serves as a reference to correct the lower-precision inertial measurements. Rather than treating both sources equally, the system uses the terrain database as the authoritative reference to asymmetrically correct the inertial drift, achieving high precision while maintaining continuous position data.
4Adaptability or versatility
If opportunity landmarks are created dynamically from terrain database, then mission preparation is eliminated and adaptability increases, but the complexity of real-time processing increases
Solution Approach 1:
The system performs real-time opportunity landmark generation automatically using its current position and the terrain database, without requiring external input or complex real-time image analysis. This self-service approach achieves high adaptability while keeping real-time processing relatively simple.
Solution Approach 2:
The system extracts only the essential information needed for recalibration from the terrain database (position coordinates of opportunity landmarks) rather than processing entire terrain images or features in real-time. This extraction approach maintains operational adaptability while significantly reducing real-time processing complexity.
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
This method enables precise location and orientation of the image sensor without mission preparation, utilizing any notable ground feature, ensuring accurate tracking and reducing errors in geographical positioning and orientation, even in environments lacking practical landmarks.
Implementation Method 1
a precise location step of the sensor (100) determined from the geographical coordinates of this landmark of opportunity (95) tracked, by a Kalman filter fed by several measurements of distances between the sensor and the landmark of opportunity and by multiple measurements of the orientation of the LDV from the sensor to the landmark of opportunity
Implementation Method 2
a rangefinder harmonized with the optical axis of the imager, intended to be embarked on board the aircraft, the optical axis of the rangefinder defining the LDV of the sensor
Data Source
Figure 1
Figure 2
Figure 3a~3b
AI summary
The invention concerns a method for geolocating an image sensor having an LDV and installed on board an aircraft. The geographical position of the sensor and the orientation of the LDV of said sensor being approximate, it comprises: a step of creating a opportunity daymark comprising the following sub-steps: on a screen for displaying acquired images, with the axis of a telemeter being marked in said images by means of a crosshairs of which the direction represents the LDV, marking, by an operator, a stationary element on the ground, moving of the LDV, by the operator, in order to bring the crosshairs over this stationary element, tracking of said stationary element, estimating of the approximate geographic position of this stationary element, searching, in a terrain database, for the location corresponding to an area centred on the stationary element, displaying a terrain image of this location and marking of the stationary element by the operator, pointing, by the operator, of this stationary element in the displayed terrain image, and recovering, from the terrain database, the pointed geographical coordinates, said stationary element becoming an opportunity daymark, the sensor moving relative to the daymark, a step of accurately locating the sensor from the geographical coordinates of this daymark and using a Kalman filter supplied with a plurality of measurements of distances between the sensor and the daymark and with a plurality of measurements of the orientation of the LDV of the sensor towards the daymark, with a measurement of the orientation for each telemetry measurement, making it possible to simultaneously accurately estimate the orientation of the LDV.