MIMO-SAR Microwave Imaging With Compressed Multi-Coset Range Migration
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Solution Overview
Problem
Conventional microwave imaging systems face challenges in achieving high-resolution imaging with reduced acquisition time and computational efficiency, particularly in compressed scanning scenarios using MIMO-SAR, due to the limitations of existing SAR reconstruction algorithms like BPA, RMA, and CS paradigms.
Innovation Solution
A method and system employing a compressed multi-coset range migration technique for MIMO-SAR that involves forming sub-arrays from intermittently skipped periodic blocks, applying 2D FFT, leveraging block-sparsity constraints, and using a denoising convolutional neural network (DnCNN) for enhanced image reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Back Projection Algorithm (BPA) is used for SAR reconstruction, then image quality is improved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the measurement matrix into multiple sub-matrices based on the multi-coset sampling structure. By dividing the large-dimensional measurement data into smaller manageable blocks, the reconstruction algorithm can process each segment efficiently while maintaining overall image quality, thus reducing computational complexity compared to processing the entire matrix with BPA.
Solution Approach 2:
The patent replaces the computationally intensive BPA mechanical processing with an optimized algorithmic approach using multi-coset sampling and block-sparsity constraints. This substitution uses mathematical transformations and constraints to achieve similar reconstruction quality with significantly reduced computational burden.
2Productivity
If classical Range Migration Algorithm (RMA) is used for SAR reconstruction, then computational efficiency is improved, but uniform array placement and consistent SAR trajectory are required
Solution Approach 1:
The patent changes the sampling parameter structure by introducing multi-coset sampling with intermittent skipping patterns. This allows the system to operate with non-uniform array placements and inconsistent SAR trajectories while maintaining computational efficiency similar to classical RMA, thus improving adaptability without sacrificing productivity.
3Quantity of substance
If compressed sensing (CS) paradigm-based approach with Single Measurement Vector-compressed sensing (SMV-CS) is used, then measurement dimension is reduced, but computational complexity increases due to large dimensional measurement matrices
Solution Approach 1:
The patent segments the large-dimensional measurement matrix into multiple smaller sub-matrices based on the multi-coset sampling structure. By dividing the compressed sensing problem into manageable blocks, the algorithm reduces computational complexity while maintaining the measurement dimension reduction benefits of compressed sensing.
Solution Approach 2:
The patent applies block-sparsity constraints to only the essential parts of the measurement matrix that contain the most critical information. This partial action approach processes only the necessary sub-matrices with full sparsity constraints, while other parts are handled more efficiently, reducing overall computational complexity compared to applying constraints to the entire large-dimensional matrix.
4Adaptability or versatility
If Non-Uniform FFT based RMA (NUFFT-RMA) is used for image reconstruction, then non-uniform array requirements are relaxed, but noticeable artifacts result in degraded SAR image reconstruction
Solution Approach 1:
The patent changes the sampling parameters by introducing controlled intermittent skipping patterns in the multi-coset framework. This parameter modification allows the system to achieve uniform effective sampling from non-uniform physical array placements, relaxing hardware requirements while avoiding the artifacts that plague NUFFT-RMA approaches.
5Productivity
If SAR acquisition time is reduced through compressed scanning, then productivity is improved, but existing reconstruction algorithms become unsuitable
Solution Approach 1:
The patent changes the sampling acquisition parameters by implementing intermittent skipping patterns in the multi-coset framework. This allows compressed scanning to be performed while maintaining algorithm compatibility with range migration techniques, thus improving productivity without sacrificing algorithm suitability.
Solution Approach 2:
The patent segments the compressed scanning acquisition into multiple periodic blocks that can be processed independently through the multi-coset framework. This segmentation allows existing reconstruction algorithms to handle the compressed data efficiently, maintaining compatibility while achieving faster acquisition speeds.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The approach significantly reduces SAR acquisition time, improves computational efficiency, and enhances image quality by mitigating artifacts, resulting in high-resolution microwave imaging.
Implementation Method 1
receiving, via one or more hardware processors, a plurality of back-scattered signals from a target scene
Implementation Method 2
a compressed scanning acquisition setup comprises an array of sensors that captures a plurality of measurements
Implementation Method 3
computing, via the one or more hardware processors, a Fourier transform (FT) of the plurality of sub-array elements by deploying a two-dimensional Fast Fourier transform (2D-FFT)
Data Source
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AI summary
There is a need to address the problem of compressed scanning in SAR acquisitions for microwave imaging. Embodiments of the present disclosure provide microwave imaging with multiple-input and multiple-output synthetic aperture radar using compressed multi-coset range migration technique. The present disclosure reduces the SAR acquisition time by compressed scanning, where a compressed scanning acquisition setup captures radar measurements to obtain a microwave image of a target scene by intermittently skipping blocks during SAR acquisition. Further, reconstruction of a microwave image of a target scene is performed using 2D Fourier Transform (FT) of the radar measurements based on a multi-coset range-migration framework. The multi-coset range migration framework makes use of intermittent scanning and formulates a compressed sensing-based architecture by leveraging block-sparsity constraints. Subsequently, a denoising convolutional neural network (DnCNN) is used to enhance and denoise the reconstructed microwave image to obtain a high-resolution image of the target scene.