Broadband radar data fusion method based on rapid sparse Bayesian learning algorithm

Abstract

The invention discloses a broadband radar data fusion method based on a rapid sparse Bayesian learning algorithm. Modeling is performed on data via adopting a geometrical theory of diffraction by aiming at multiband scattered field data of multiple radars which are distributed in a same place so that a problem of radar data fusion is converted into the problem of sparse expression, and the problem of sparse expression is solved by utilizing the rapid sparse Bayesian learning algorithm. Firstly extrapolation is performed on sub-band data of different radars so that overlapping frequency band data are obtained. Then coherent registration is performed on the sub-band data of different radars according to the overlapping frequency band data. Finally, frequency band extrapolation and interpolation is performed by utilizing the sub-band data after coherent registration so that ultra broadband data are obtained, and distance direction resolution of the radars is enhanced.

Description

technical field [0001] The invention belongs to the field of radar signal processing, in particular to a wideband radar data fusion method based on a fast sparse Bayesian learning algorithm. Background technique [0002] Wideband radar is widely used in target recognition, radar imaging, and missile defense because of its high range resolution, but the details of typical radar targets (such as ballistic missiles, aircraft, satellites, etc.) There is a range resolution unit of wideband radar. The distance resolution of radar is determined by the bandwidth. The wider the bandwidth, the higher the distance resolution. Therefore, the bandwidth of the existing wideband radar must be further improved. There are two ways to achieve this goal: one is to upgrade the existing wideband radar, but due to the technical difficulty and high cost of designing ultra-wideband radar, this way is not easy to implement; the other way is to use radar data fusion technology to Multiple broadband ...

AI Technical Summary

Problems solved by technology

There are two ways to achieve this goal: one is to upgrade the existing wideband radar, but due to the technical difficulty and high cost of designing ultra-wideband radar, this way is not easy to implement; the other way is to use radar data fusion technology to Multiple broadband radar echo data working in different frequency bands are processed to obtain ultra-wideband radar echoes. Obviously, if the radar data fusion technology is stable and reliable enough, this approach is the most economical and effective

[0003] A spectral estimation based on Theoretical broadband radar data fusion method, this method uses the all-pole model to model the data, and uses the root-music algorithm to estimate the model parameters, so as to realize data fusion. This method has two defects: (1) the all-pole model can only Only when the bandwidth is small can the echo be accurately represented. When the echo data is relatively large, there will be errors in modeling using the all-pole model; (2) the model order, that is, the number of scattering centers, is difficult to determine, resulting in false images in the imaging results Scattering center or absence of scattering center

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