SAR back projection imaging method based on feature distance subspace

Abstract

The invention discloses an SAR back projection imaging method based on feature distance subspace. The method is on the basis of distance history vector, and the concept of distance history vector match degree is put forward from the angle that whether a conventional BP algorithm can focus; and then, based on minimum mean square error criterion and principal component analysis principle, the concept of feature distance subspace is put forward in the SAR imaging field. The measured distance history vector is optimally corrected in the feature distance subspace, so that measurement noise is suppressed very well, the distance history vector match degree is improved, compensated phase position is subjected to accurate estimation, and finally, good focusing effect is obtained. The SAR back projection imaging method is suitable for SAR two-dimension imaging and three-dimensional imaging of any system, and thus the method has very good popularization performance. Finally, since correcting the distance history vector in the feature distance subspace is a linear operation, the method has very high operability.

Description

Technical field: [0001] The technical invention belongs to the technical field of radar, and in particular relates to the technical field of synthetic aperture radar (SAR) imaging. Background technique: [0002] Among the many synthetic aperture radar (SAR) imaging algorithms, the back projection (BP) algorithm is famous for its strong adaptability and excellent focusing performance (see reference "P. Zhang, X. Zhang, and G. Fang. Comparison of the imaging resolutions of time reversal and back-projection algorithms in EM inverse scattering. IEEE Geoscience and Remote Sensing Letters, 2013, 10(6):357-361"). However, in reality, since the distance history used for phase compensation can only be obtained through actual measurement, this distance history is inevitably disturbed by measurement noise. Because the phase error caused by the measurement noise will directly affect the focusing effect of the BP algorithm, only by accurately estimating the phase error can the BP algori...

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Problems solved by technology

However, the first type of self-focusing algorithm relies too much on the strong point target in the scene, and the second type of self-focusing algorithm belongs to the image domain algorithm in essence, they do not estimate the phase compensation from the perspective of directly suppressing the measurement noise

[0004] How to estimate the phase of compensation by directly suppressing the measurement noise, so as to achieve the purpose of self-focusing of the BP algorithm, as far as the inventor knows, there is no published literature on this direction.

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