Radar Feature Extraction via Sub-domain Partitioning
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Solution Overview
Problem
Current radar imaging systems face challenges in efficiently processing and interpreting vast amounts of data from radiative near-field scenes, particularly in determining scattering coefficients and classifying objects within these scenes, due to the complexity of wave-based imaging and the need for high-resolution images from sparse data.
Innovation Solution
The system employs a partitioning scheme that groups sensor elements and scene data into sub-domains, using block diagonal transfer matrices to efficiently map and invert measurements, allowing for the computation of intermediate scattering coefficients and object parameters, which represent reflective properties and enable object classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If wave-based imaging is used to collect measurements from the scene, then measurement information is obtained, but computational complexity increases due to millions of measurements and voxels
Solution Approach 1:
The patent divides the scene into multiple sub-domains and groups voxels into super-voxels, creating a hierarchical structure that reduces the computational burden. By processing smaller sub-domains independently and combining results, the system handles millions of measurements without requiring full-scene computational complexity.
Solution Approach 2:
The patent introduces an intermediate domain between measurements and scattering coefficients, creating a multi-dimensional processing space. This intermediate representation allows the system to process data in a transformed space that reduces computational requirements while preserving essential information.
2Measurement precision
If high-resolution images are computed from sparse data, then image quality improves, but processing time increases
Solution Approach 1:
The patent performs preliminary processing by computing intermediate scattering coefficients and organizing data into sub-domains before final image reconstruction. This pre-processing step prepares the data in an optimized format that accelerates the subsequent high-resolution imaging process.
Solution Approach 2:
The patent introduces intermediate scattering coefficients as a mediator between raw measurements and final high-resolution images. This intermediate representation serves as a computational bridge that reduces the direct complexity of generating high-resolution images from sparse measurements.
3Productivity
If scene is divided into sub-domains and mappings are applied, then computational efficiency improves, but system complexity increases
Solution Approach 1:
The patent segments the scene into sub-domains and applies localized mappings to each segment. This segmentation allows independent processing of smaller regions with simpler mappings, improving overall computational efficiency while managing system complexity through modular organization.
Data Source
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AI summary
Data is received characterizing a measurement for a scene received by a plurality of sensor elements forming a sensor array. Intermediate scattering coefficients can be determined using the received data by applying to the received data a mapping of a plurality of scene sub-domains to the plurality of sensor elements. A parameter of an object within the scene can be estimated using the intermediate scattering coefficients. Related apparatus, systems, techniques, and articles are also described.