Dark room broadband RCD measurement method based on compression sensing

Abstract

The invention discloses a dark room broadband RCD measurement method based on compression sensing. The method comprises steps of (1) designing a sparse measurement matrix, and configuring compression sampling testing parameters; (2) placing a scaling body and a measurement object on a rotary table in sequence, and completing echo data sampling of the measurement object, wherein the rotary table rotates at the uniform speed in the sampling process; (3) completing scaling processing to the measurement object according to the obtained sampling data; (4) subjecting object data after scaling to sparseness by the sparse measurement matrix; and (5) using a two-dimensional data reconstruction algorithm to perform data reconstruction of other angles and other frequencies. The method utilizes the coupling of the two-dimensional signal, and improves the imaging resolution under a precondition of the same data size.

Description

technical field [0001] The invention relates to the measurement field, in particular to a darkroom broadband RCS measurement method based on compressed sensing. Background technique [0002] In the traditional broadband RCS measurement method, in order to make the target expansion unambiguous in the range and azimuth directions, the measurement frequency interval and angle step size must be limited by the Nyquist sampling theorem. Information acquisition, storage, transmission and processing under the constraints of this theory have been It has become one of the main bottlenecks in the further development of the current information processing field. Different from Nyquist sampling, Compressed Sensing (CS) does not directly measure the signal itself, but uses the construction observation matrix Φ to project a sparse or compressible high-dimensional signal onto the low-dimensional space, and the measured value is the signal from the high-dimensional space Projected values ​​i...

AI Technical Summary

Problems solved by technology

A single algorithm often has both advantages and disadvantages. For example, the greedy algorithm runs fast, but requires a lot of measurement data and has low precision; the convex optimization algorithm requires less measurement data and has high reconstruction accuracy, but has a heavy computational burden.

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