Super-Resolution Aimpoint Tracking for Moving Laser Targets
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Solution Overview
Problem
High-energy laser systems face challenges in maintaining beam aim on moving targets due to varying target aspects and sizes, requiring rapid and accurate aimpoint recognition and tracking to achieve desired results efficiently.
Innovation Solution
The system employs super-resolution imaging techniques to generate high-resolution images of targets using multiple high-speed input images, identifying edges and aimpoints, and tracks these aimpoints over time, adapting to shape changes without relying on target type libraries or 3D models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional imaging sensors are used to capture target images, then the system structure remains simple, but the resolution is insufficient to accurately identify aimpoints on moving targets
Solution Approach 1:
The patent segments the image processing task into multiple stages: capturing multiple low-resolution images at different times, upsampling each image individually, aligning them through correlation, and combining them to produce a high-resolution result. This segmentation allows the system to achieve high measurement precision without requiring a single complex high-resolution sensor.
Solution Approach 2:
The patent transitions from spatial dimension (pixel resolution) to temporal dimension (multiple frames over time). By capturing multiple images at different time points and processing them sequentially, the system achieves super-resolution without needing a single high-resolution sensor, thus managing device complexity while improving measurement precision.
2Measurement precision
If the imaging sensor pixel spacing is increased to improve resolution, then aimpoint identification accuracy improves, but the field of view decreases and tracking speed reduces
Solution Approach 1:
The patent segments the resolution requirement across multiple time steps. Each individual image can be captured at a lower resolution with faster frame rates, but by combining multiple segmented frames through upsampling and correlation, the system achieves high resolution without sacrificing tracking speed.
Solution Approach 2:
The patent performs preliminary upsampling and alignment operations on individual frames before final combination. This preliminary processing enables the system to work with lower-resolution input images that can be captured at higher frame rates, while still producing high-resolution output through subsequent processing steps.
3Measurement precision
If multiple high-speed images are captured and processed to create super-resolution images, then aimpoint recognition accuracy improves, but processing time increases
Solution Approach 1:
The patent segments the processing workload by handling each image frame independently through upsampling and correlation operations, then combining the results. This segmentation allows parallel processing of multiple frames and enables optimization of each step without requiring the entire process to complete sequentially, reducing overall processing time while maintaining high measurement precision.
Solution Approach 2:
The patent applies partial upsampling and correlation operations to each frame rather than requiring perfect alignment and processing of all frames with full precision. This partial action approach reduces the computational burden on individual operations while still achieving sufficient super-resolution for accurate aimpoint identification.
4Productivity
If the system processes images in real-time to track moving targets, then target tracking capability improves, but the computational load increases
Solution Approach 1:
The patent segments the computational tasks into independent operations that can be applied to each frame sequentially: upsampling, correlation, and combination. This segmentation enables the system to process frames at a manageable rate while maintaining real-time tracking capability, as each segment can be optimized independently and results can be combined efficiently.
Solution Approach 2:
The patent implements a dynamic processing approach where the system adapts to varying target motion characteristics. By using correlation to align frames and adjusting processing parameters based on detected motion patterns, the system maintains high productivity while managing computational complexity through adaptive rather than fixed processing rates.
Data Source
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AI summary
A system includes at least one imaging sensor (228, 378) configured to capture images of a target (104). The system also includes at least one controller (230) configured to generate super-resolution images of the target using the captured images and identify multiple edges of the target using the super-resolution images. The at least one controller is also configured to identify an aimpoint on the target based on the identified edges of the target. In addition, the at least one controller is configured to update the aimpoint on the target as the target moves over time. The system may further include a high-energy laser (HEL) (202, 302) configured to generate an HEL beam (106) that is directed towards the target, and the at least one controller may be configured to adjust one or more optical devices (218, 220, 224, 342, 348, 354, 364) to direct the HEL beam at the identified aimpoint on the target.