Super-Resolved Image Tracking for Missile Guidance
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Solution Overview
Problem
Existing tracking technologies, particularly in homing missiles, face inaccuracies due to limitations in image-processing capabilities, which result in reduced accuracy and increased risk of missing the target, and are constrained by size, weight, and computational demands, especially when dealing with high-speed targets.
Innovation Solution
A method and apparatus that utilize super-resolution image processing to enhance tracking accuracy by calculating a super-resolved image from a plurality of images, de-resolving it, and correlating it with a lower quality image to improve target location identification, while being computationally efficient and capable of real-time processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If advanced image-processing equipment is added to improve tracking accuracy, then tracking precision improves, but device weight and cost increase
Solution Approach 1:
The patent combines multiple low-resolution images into a single super-resolved image through image fusion techniques. By merging information from multiple frames and applying super-resolution algorithms, the system achieves high-tracking precision without requiring heavy advanced image-processing equipment, thus resolving the contradiction between tracking accuracy and device weight
Solution Approach 2:
The patent transitions from spatial resolution enhancement to temporal dimension utilization by processing sequences of images over time. Instead of relying on single high-resolution images requiring powerful hardware, the system uses temporal super-resolution across multiple frames to achieve high tracking accuracy with lighter computational equipment
2Measurement precision
If advanced image-processing equipment is added to improve tracking accuracy, then tracking precision improves, but device cost increases
Solution Approach 1:
The patent combines multiple low-resolution images into a single super-resolved image through image fusion techniques. By merging information from multiple frames and applying super-resolution algorithms, the system achieves high-tracking precision without requiring heavy advanced image-processing equipment, thus resolving the contradiction between tracking accuracy and device weight
Solution Approach 2:
The patent transitions from spatial resolution enhancement to temporal dimension utilization by processing sequences of images over time. Instead of relying on single high-resolution images requiring powerful hardware, the system uses temporal super-resolution across multiple frames to achieve high tracking accuracy with lighter computational equipment
3Measurement precision
If computationally demanding image-processing techniques are used to improve tracking accuracy, then measurement precision improves, but processing speed decreases
Solution Approach 1:
The patent performs preliminary registration and alignment of multiple images before super-resolution processing. By pre-aligning images using feature matching and transformation matrices, the system reduces computational complexity during the actual super-resolution phase, enabling both high target location accuracy and real-time processing speeds required for fast-moving targets
Solution Approach 2:
The patent segments the image-processing task into distinct stages: image registration, feature extraction, super-resolution computation, and correlation tracking. This segmentation allows optimization of each stage independently, with computationally intensive super-resolution applied only to registered image sequences rather than raw images, thereby maintaining both precision and processing speed
4Measurement precision
If more images are processed to improve super-resolution quality, then measurement precision improves, but loss of time increases
Solution Approach 1:
The patent implements periodic processing where super-resolution is applied at specific intervals rather than continuously to every frame. By processing image sequences at optimized intervals and using motion compensation between frames, the system maintains high target location accuracy while reducing overall processing time and enabling real-time tracking of fast-moving targets
Data Source
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Figure 5(a)
AI summary
In a method of tracking an object, a plurality of images of a target object is obtained. A super-resolved image of the target object is calculated from the plurality of images. A further image of the target object is obtained. The further image is correlated with the super-resolved image, in order to identify the location of the target object in the further image.