SAR Autofocus Subimage Segmentation for Position Accuracy
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Solution Overview
Problem
Current methods for determining the position and orientation of objects in radar images, particularly ships, suffer from low resolution and inaccuracies due to linear motion assumptions and radial acceleration limitations, which restrict their effectiveness in capturing precise changes in distance and orientation.
Innovation Solution
A method that divides radar images into subimages, applies an autofocus technique to each subimage to determine changes in distance, and calculates the position and orientation of objects by analyzing these changes, allowing for high-resolution determination of object position and orientation using synthetic aperture radar data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a CFAR detector is used to determine object position in radar images, then the method can identify objects with low resolution, but the position determination accuracy deteriorates due to inability to resolve similar objects
Solution Approach 1:
The radar image is divided into multiple subimages, and an autofocus method is applied to each subimage to determine local phase errors and distance changes. This segmentation allows precise position determination for each object without being confused by similar objects in other regions, thereby improving measurement precision while managing complexity through localized processing
2Measurement precision
If a backprojection autofocus method is used to determine phase error, then good results are achieved for linear motion, but the method fails when radial acceleration exceeds range resolution
Solution Approach 1:
The method dynamically adapts to different motion conditions by applying autofocus to multiple subimages and combining their phase error information. This allows the system to handle both linear motion and non-linear motion with radial acceleration exceeding range resolution, improving adaptability while maintaining precision through multi-subimage processing
3Measurement precision
If the radar image is processed as a whole, then computational efficiency is maintained, but the resolution and detail of object position determination deteriorates
Solution Approach 1:
The image is segmented into subimages processed in parallel, which improves resolution by allowing localized autofocus analysis. The processing efficiency is maintained through parallel computation of phase errors across multiple subimages, achieving high resolution without significant productivity loss
Data Source
AI summary
The disclosure concerns a method for determining a change in position and orientation of an object using data from a synthetic aperture radar (SAR) with the following steps: acquiring a radar image containing the object; dividing the radar image into at least two subimages; acquiring short radar information comprising a first reflected pulse, which has been recorded by the radar and/or a quantity derived therefrom, the first reflected pulse having information about image data of the radar image; for the short radar information, performing an autofocus method which determines a change in distance for each of the at least two subimages; and determining the change in position and orientation of the object given for each point of the object by the respective change in distance of the corresponding subimage.