Corrected Quadtree Back-Projection for Low-Distortion Near-Field Imaging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing radar imaging technologies, such as Doppler-Beam Sharpening and Quadtree Back-Projection, struggle with distortion and high computational costs when imaging large targets at near-field distances due to unaccounted wave front curvature, particularly in mmWave security scanners.
Innovation Solution
A method for near-field radar image reconstruction that segments the image into sub-images, corrects for wavefront curvature by adjusting sub-image centers using equations (3) and (4), and performs iterative back-projection and upsampling to reduce distortion while maintaining computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If standard Quadtree Back-Projection is used for computational efficiency, then processing speed is improved, but image quality deteriorates due to unaccounted wave front curvature
Solution Approach 1:
The patent divides the image into multiple sub-images and processes them separately with different curvature corrections. This segmentation allows the algorithm to apply computationally efficient corrections to specific regions while maintaining overall processing speed, resolving the contradiction between productivity and image quality.
Solution Approach 2:
The patent applies different curvature correction strategies to different regions of the image based on their specific characteristics. By making the correction quality local rather than uniform, the system achieves high image quality where needed while maintaining computational efficiency in other regions.
2Manufacturing precision
If full Back-Projection is used to account for wave front curvature, then image quality is improved, but computational cost increases significantly
Solution Approach 1:
The patent applies partial curvature correction only to the extent necessary for near-field imaging accuracy. By using approximate corrections for sub-images rather than full exact corrections, the system achieves sufficient image quality while avoiding the O(N³) computational cost of complete Back-Projection.
Solution Approach 2:
The patent modifies the curvature correction parameters based on the specific imaging conditions (near-field vs. far-field). By changing the correction parameters adaptively, the system achieves accurate imaging when needed while maintaining computational efficiency in standard scenarios.
3Loss of time
If image segmentation is applied to reduce computation, then processing time is reduced, but distortion increases due to wave front curvature effects
Solution Approach 1:
The patent applies preliminary curvature correction to sub-image centers before the actual back-projection process. By performing this correction in advance, the system eliminates distortion that would otherwise accumulate during processing, maintaining shape accuracy while benefiting from reduced processing time.
Solution Approach 2:
The patent introduces intermediate correction terms that mediate between the segmented sub-images and the final reconstructed image. These intermediary corrections ensure that wave front curvature effects are properly accounted for across sub-image boundaries, preventing distortion while maintaining computational efficiency.
Data Source
AI summary
Provided is a method of near-field radar image reconstruction from radar data, the method comprising receiving radar data corresponding to an image, segmenting the image into multiple sub-images, wherein each sub-image comprises a section of the image, and for each sub-image, generating shifted data, by multiplying the radar data by a shifting term, wherein the shifting term accounts for a distance between the centre of the image and a centre of the sub-image, wherein the centre of the sub-image is corrected for a curvature of a wavefront of a radar pulse, generating reduced data by filtering and downsampling the shifted data, such that the total amount of the shifted data is reduced and reconstructing the sub-image, by back-projecting the reduced data, wherein performing the shifting by using the shifting term reduces distortion in the reconstructed sub-image.