Video Geolocation Pointing Error Correction via Digital Stabilization
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Solution Overview
Problem
Conventional persistent video sensors face challenges in interpreting targets and features due to platform motion-induced changes in scale, perspective, and viewing geometry, requiring computationally intensive methods for error correction in pointing solutions.
Innovation Solution
A closed-loop system that digitally transforms image frames to compensate for platform motion, calculates residual transformation coefficients to correct pointing errors, and provides feedback for perfect staring, enhancing image quality and SNR by stabilizing the imagery as if the platform were stationary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional persistent video sensors are used to capture images from a moving platform, then the sensor can collect video data of a target, but the platform motion causes changes in scale, perspective, and viewing geometry that complicate or prevent accurate interpretation of targets and features
Solution Approach 1:
The system employs feedback mechanisms where motion residuals from digitally transformed frames are calculated and used to compute residual transformation coefficients. These coefficients are then fed back to apply additional corrections to compensate for pointing errors, creating a closed-loop system that iteratively improves measurement precision by using the output of one processing stage as input for the next correction stage
Solution Approach 2:
The patent introduces intermediate processing steps including digital transformation of frames to a common field of view, calculation of motion residuals, and computation of transformation coefficients using image eigenfunctions. These intermediary computations serve as mediators between the raw motion-corrected frames and the final corrected output, breaking down the complex correction process into manageable sequential operations
2Measurement precision
If computationally intensive techniques are used to iteratively determine platform location and sensor boresight pointing, then pointing error correction can be achieved, but the computational burden and processing time increase significantly
Solution Approach 1:
The correction process is segmented into distinct modular stages: (1) digital transformation of frames to a common field of view using known trajectory and pointing information, (2) calculation of motion residuals from the transformed frames, (3) fitting image eigenfunctions to residuals to compute transformation coefficients, and (4) application of these coefficients to compensate for pointing errors. This segmentation allows each module to be optimized independently and enables parallel processing where applicable
Solution Approach 2:
The system performs preliminary digital transformation of image frames using previously known trajectory and sensor pointing information before the actual pointing error correction is applied. This preliminary action prepares the data in a standardized form (common field of view with consistent pixel size and orientation) that facilitates more efficient subsequent processing and reduces the computational burden of the final correction step
3Stability of the object's composition
If the sensor attempts to maintain perfect staring at a ground point while the platform moves, then persistent observation can be achieved, but platform motion induces apparent motion in the captured frames that degrades image quality
Solution Approach 1:
The patent replaces mechanical stabilization systems with digital image processing techniques. Instead of using additional mechanical gimbals or active optical stabilization components to physically counteract platform motion, the system uses digital transformation and computational methods to correct for motion effects in the captured frames, achieving stable persistent observation through software-based compensation rather than hardware mechanisms
Solution Approach 2:
The system dynamically changes multiple parameters including the field of view transformation, pixel size scaling, orientation rotation, and transformation coefficients based on the calculated motion residuals. By adjusting these parameters in response to measured motion effects, the system maintains stable persistent observation while compensating for platform-induced apparent motion in the captured imagery
Data Source
AI summary
Systems and methods for identifying root causes for and/or correcting pointing error in moving platform imaging. Scene frames captured by a sensor (e.g., a focal plane array, etc.) are digitally transformed to compensate for relative motion between the scene and platform, then motion residuals are computed based on inter-frame scene gradients, and image eigenfunctions are fit to the motion residuals to compute coefficients that may be used to efficiently correct future image acquisition, determine root cause(s) of pointing (e.g., sensor pointing error, scene mean altitude, platform altitude, etc.) errors, and further digitally correct the captured images. Comparisons may be made to a database of residual transformation coefficients based on known or expected relative motion of the platform to the scene and a known or expected pointing angle. Truly moving targets may be identified, removed and re-added after image digital transformation processing.