Georegistration Refinement for SAR Image Distortion Reduction
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Solution Overview
Problem
Existing systems for generating images from raw sensor data suffer from distortions due to uncertainties in sensor location and errors in the image registration process, particularly in applications like Synthetic Aperture Radar (SAR), where foreshortening and layover distortions are prevalent.
Innovation Solution
A system and method that retains raw sensor data and uses inertial navigation estimates to generate an enhanced image by iteratively refining the sensor inertial states through georegistration, combining the raw data with updated estimates to reduce distortions and improve image accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If image registration is performed using initial sensor location estimates, then image generation can proceed, but image distortion occurs due to uncertainties in sensor location and registration errors
Solution Approach 1:
The patent implements a feedback mechanism where the generated image is used to update the sensor inertial state estimates through georegistration. The updated estimates are then fed back into the image generation process, creating an iterative refinement loop that progressively reduces image distortion and improves accuracy.
Solution Approach 2:
The system performs preliminary image generation using initial (albeit uncertain) sensor location estimates before the final enhanced image is produced. This preliminary action allows the system to create an intermediate image that can then be used to refine the location estimates, rather than waiting for perfect location data before any image processing occurs.
2Manufacturing precision
If raw sensor data is retained for reprocessing, then enhanced image quality can be achieved through iterative refinement, but data storage requirements increase
Solution Approach 1:
The system performs preliminary image generation and georegistration steps that produce updated sensor inertial state estimates. These estimates are then used to reprocess the retained raw sensor data, allowing the same data to be processed multiple times with improving accuracy rather than requiring additional data collections.
3Manufacturing precision
If iterative georegistration is performed to update sensor inertial states, then distortion reduction is achieved, but processing time increases
Solution Approach 1:
The system uses feedback from the generated image to update sensor inertial state estimates through georegistration. This feedback loop allows the system to iteratively improve image accuracy by reducing distortions such as foreshortening and layover, with each iteration refining the estimates based on the previous image output.
Data Source
AI summary
A method and apparatus for generating an image from raw sensor data. In one embodiment, the method comprises reading a plurality of raw sensor data sets from one or more sensors at a plurality of sensor inertial states, generating an estimate of each of the plurality of sensor inertial states, and while retaining each of the raw sensor data sets, generating an image, the image generated at least in part from the plurality of estimated sensor inertial states and the plurality of raw sensor data sets, and generating an updated estimate of at least one of the sensor inertial states, the updated estimate of the at least one of the sensor inertial states generated at least in part from the generated image and the plurality of estimated sensor inertial states. Finally, an enhanced image is generated from the retained raw sensor data sets and the updated estimate of the at least one of the sensor inertial states.