Interpolation Method for Complex RCS Data Based on Compressed Sensing

Abstract

The invention relates to a complex RCS data interpolation method based on compressed sensing, and belongs to the technical field of comprehensive testing. A specific implementation of the method includes: constructing a complex domain reconstruction model according to the RCS measurement data; solving the complex domain reconstruction model to obtain the sparse distribution of the RCS data; reconstructing target frequency domain data based on the sparse distribution , realize complex RCS data interpolation. Compared with the traditional method of simply diagonalizing complex matrices, the invention does not reduce or increase the original constraint range, has fast processing speed and high precision, and can effectively shorten measurement time to improve RCS measurement efficiency.

Description

technical field [0001] The invention relates to the technical field of comprehensive testing, in particular to a complex RCS data interpolation method based on compressed sensing. Background technique [0002] Traditional interpolation algorithms mainly include spline interpolation and Fourier transform method. Spline interpolation is a real number method and a pure interpolation algorithm. For darkroom RCS measurement data, it lacks physical concept and physical meaning support. The Fourier transform method has two defects. One is limited by the Nyquist sampling theory. To make the target expansion unambiguous in the distance and azimuth directions, the amount of sampled data cannot be reduced. The second is that the Fourier transform method is an overall algorithm. Even if the target signal is only in a small section of the unambiguous window, it must be transformed as a whole, and the algorithm efficiency is low. The radar target mostly satisfies the scattering center mo...

AI Technical Summary

Problems solved by technology

A common practice for complex field data is to convert the complex matrix to diagonal real numbers. The problem is that the original circular constraint space for the complex field has become a square constraint space, which is equivalent to increasing the scope of the constraint space, which will inevitably increase the noise of the algorithm.

A common practice for two-dimensional matrix data is to use the matrix vectorization method of stacking rows and columns. The disadvantage is that the dimension of the compressed sensing measurement matrix is ​​greatly increased by a geometric order, which greatly increases the computational memory and time consumed by the algorithm.

Images