Distributed Radar Image Formation via Data Partitioning
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Solution Overview
Problem
Current methods for radar image formation using imaging radar require processing large datasets, which is not timely or cost-effective, and do not accommodate the efficient handling of such large amounts of data.
Innovation Solution
A distributed network system that processes large amounts of radar imaging data across multiple computational nodes, where the data is partitioned into subarrays and transformed using algorithms like FFT, warping, or autofocus, allowing for parallel processing and efficient image formation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large datasets are processed using current radar image formation methods, then image quality is maintained, but processing time and computational cost increase significantly
Solution Approach 1:
The patent divides the large radar dataset into multiple subarrays and distributes them across a network of computational nodes. Each node processes a portion of the data independently using algorithms like FFT, warping, or autofocus transforms. The processed subarrays are then recombined to form the complete image, enabling parallel processing that reduces overall computation time while maintaining image quality.
2Measurement precision
If large datasets are processed using current radar image formation methods, then complete image data is obtained, but computational cost and storage requirements increase
Solution Approach 1:
The patent segments the large dataset into smaller subarrays that are distributed across multiple computational nodes. Each node stores and processes only its assigned portion of the data, reducing the storage burden on any single system. The distributed architecture allows the complete image to be reconstructed from the combined results of all nodes, maintaining image completeness while reducing overall storage requirements through parallel distribution.
3Device complexity
If data processing is centralized in current methods, then processing simplicity is maintained, but processing speed and efficiency decrease
Solution Approach 1:
The patent transitions from a centralized single-node processing architecture to a distributed multi-node network architecture. This dimensional change in system organization allows parallel processing of data subarrays across multiple nodes simultaneously. The networked distributed system achieves higher processing efficiency and productivity while managing complexity through standardized communication protocols and data distribution mechanisms.
Data Source
AI summary
A system is provided for synthetic aperture radar image formation within a distributed network. A radar antenna receives successive echoes of a plurality of pulses of radio waves transmitted in an environment of a target. A processing system defines, from the successive echoes, an array of data elements representing a density of a reflective surface of the target at locations within the environment. The processing system also partitions the array into a plurality of subarrays based on a predefined array partitioning scheme. A respective node of a plurality of nodes receives and applies at least one algorithmic transform to a subarray of the plurality of subarrays, and determines a respective portion of a volume of space occupied by the target based thereon. The respective portion is combinable with other respective portions to determine the volume of space and thereby form an image of the target.