Signal Processing Device Compressed Sensing Segmentation
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Solution Overview
Problem
Computationally demanding processes in signal processing for high-resolution imaging with active sensors, such as synthetic aperture radar, result in long processing times, making real-time detection challenging.
Innovation Solution
A signal processing device and method that reduces computation by generating a reception signal matrix with a sparse vector, using a range processor to specify ranges in the imaging matrix, and a reconstruction processor to perform reconstruction processing, while maintaining resolution through synthesis of the sparse vector across moving ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If compressed sensing processing is executed to achieve high-resolution imaging, then image resolution is improved, but processing time increases significantly
Solution Approach 1:
The patent divides the imaging matrix into multiple ranges in the first direction and processes each range separately using compressed sensing. The range processor specifies ranges, and the reconstruction processor performs reconstruction processing on each range's sparse vector independently. This segmentation reduces the computational burden per iteration while maintaining overall image resolution.
Solution Approach 2:
The patent performs reconstruction processing on a subset of ranges rather than the entire imaging matrix simultaneously. By processing ranges partially and iteratively, the system achieves sufficient resolution without the computational burden of processing the complete high-resolution image at once.
2Measurement precision
If the number of divisions in the first direction is increased to maintain resolution, then image quality is improved, but computation complexity increases
Solution Approach 1:
The imaging matrix is segmented into multiple ranges along the first direction, with each range processed independently. This segmentation allows the system to maintain resolution through multiple divisions while reducing computation complexity by processing smaller sub-matrices rather than the entire large matrix at once.
Solution Approach 2:
The patent dynamically adjusts the number of divisions and processing ranges based on the specific imaging requirements. The range processor can specify different numbers of ranges and the reconstruction processor can adapt the reconstruction parameters, allowing flexible computation complexity management while maintaining desired resolution.
Data Source
AI summary
A signal processing device includes: a data receiver configured to transmit a transmission wave a position of which changes in a first direction and which spreads in a second direction orthogonal to the first direction, and generate a matrix of acquired observation data in the first and second directions, as a reception signal matrix, with a value of a position in the reception signal depending on a signal strength; a range processor configured to specify a range in the second direction in an imaging matrix, and set a sparse vector including a component in the first direction of the specified range; a reconstruction processor configured to perform reconstruction processing using the reception signal matrix and the sparse vector to calculate a component of the sparse vector; and a synthesis processor configured to synthesize the resulting sparse vector while moving the range, to generate an imaging matrix.