Scanning radar angular super-resolution imaging method

Abstract

The invention discloses a scanning radar angular super-resolution imaging method which comprises the following specific steps: defining an autocorrelation matrix and carrying out initialization, constructing a recursive function, finding an inverse matrix of the autocorrelation matrix, estimating azimuth parameters, calculating echo autocorrelation matrix, and judging whether iterate to a convergence state and outputting super-resolution results meeting the convergence state. According to the method in the invention, the autocorrelation matrix of azimuth signals is obtained by utilizing the convolution operation; the block tridiagonal features of the autocorrelation matrix is utilized and a divide-and-conquer algorithm, that is, the D&C algorithm, is adopted to realize quick inversion finding of the autocorrelation matrix; and finally, the weighted least square estimation is carried out on a target in an iteration manner. Compared with the prior art, the method can be suitable for relatively-low signal to noise ratio, and robust super-resolution imaging results can be obtained by utilizing single scanning data; and meanwhile, time complexity is greatly reduced, and the method is suitable for real-time signal processing.

Description

technical field [0001] The invention belongs to the technical field of radar, relates to radar imaging, and in particular relates to a scanning radar angle super-resolution method. Background technique [0002] With its advantages of strong penetrability, all-weather, large dynamic range and high imaging quality, radar imaging technology has become an irreplaceable technical means in today's detection field, and is playing an increasingly important role in civil and military fields. Scanning radar imaging is an important imaging method for radar stationary platforms and moving platforms. In scanning radar imaging, high resolution can be obtained in the range direction by matching filtering the chirp signal, however, the resolution in the azimuth direction is limited by the real aperture length. [0003] For scanning radar imaging, especially how to improve the azimuth resolution, the literature "Guan J, Huang Y, Yang J, et al. Improving angular resolution based on maximum a...

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Problems solved by technology

In scanning radar imaging, high resolution can be obtained in the range direction by matching filtering the chirp signal, however, the resolution in the azimuth direction is limited by the real aperture length

[0003] For scanning radar imaging, especially how to improve the azimuth resolution, the literature "Guan J, Huang Y, Yang J, et al. Improving angular resolution based on maximum a posteriori criterion for scanning radar[C]" (Radar Conference( RADAR), 2012IEEE.IEEE,2012:0451-0454.) proposed a maximum a posteriori deconvolution method under the Bayesian framework, using the statistical characteristics of echo and noise to establish a maximum likelihood objective function, and realizing the original The restoration of the target scene, but this method is sensitive to noise, especially the estimation variance of the low signal-to-noise ratio is large, and even false targets appear; the literature "Superresolution for Scanning Antenna" (Radar Conference, 1997, IEEE National, pp: 306-308 ) proposed a SMUSIC algorithm, which uses the echo obtained from multiple scans to estimate its second-order statistical characteristics, and uses the subspace method to super-resolution the target, but this method depends on the number of targets Prior information, and multiple scans of the target area are required; the document "Angular superresolution for real beam radar with iterative adaptive approach" (Geoscience and Remote Sensing Symposium (IGARSS), 2013, IEEE International, pp:3100-3103) proposed An angle super-resolution method based on the iterative adaptive method (IAA), which can obtain the second-order statistical characteristics of the echo by using only one scan of the echo data, so it has a lower estimation error, and this method involves Matrix multiplication and matrix inversion operations greatly affect the real-time performance of processing results

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