Forward-looking sea surface target angle super-resolution method based on sparse constraint

Abstract

The invention discloses a forward-looking sea surface target angle super-resolution method based on sparse constraint. The method comprises the following steps: S1, establishing a forward-looking imaging azimuth echo signal model; S2, based on the maximum posterior probability of the sea surface target, establishing a target function; and S3, solving the target function by using a Newton-Raphson method. According to the method, Weibull distribution more suitable for sea surface clutter characteristics is adopted to represent the sea clutter characteristics; the sea surface target sparse super-resolution imaging method comprises the steps of obtaining a sea surface target scattering coefficient, representing prior information of the sea surface target scattering coefficient by adopting Laplace distribution, deriving a target function based on a maximum posteriori criterion, solving the target function through a Newton-Raphson iterative method, obtaining a target scattering coefficient iterative solution, and realizing sea surface target sparse super-resolution imaging. According to the invention, the azimuth angle resolution of the forward-looking sea surface target imaging of the real aperture scanning radar can be significantly improved, and an idea is provided for an airborne radar sea surface target super-resolution imaging method.

Description

technical field [0001] The invention belongs to the technical field of radar imaging, in particular to a forward-looking sea surface target angle super-resolution method based on sparse constraints. Background technique [0002] With the significant increase in human sea surface activities, applications such as sea surface environment monitoring, sea surface disaster rescue, and fighter aircraft navigation urgently require aircraft to achieve high-resolution imaging of sea surface targets in the forward-looking area. The traditional synthetic aperture technology (SAR) and Doppler sharpening technology (DBS) can respectively realize high-resolution imaging of side view and oblique front view, but limited by the imaging mechanism, there are blind spots in the front view area imaging. Real-aperture scanning radar can realize forward-looking area imaging, but its azimuth resolution is limited by the antenna aperture size, and echo noise affects its imaging performance, so real-a...

AI Technical Summary

Problems solved by technology

Among them, the Weibull distribution can change the shape parameters to represent most different types of sea clutter. In addition, the difference between the Weibull distribution and the K distribution is small, but the computational complexity of the K distribution is much greater than that of the Weibull distribution.

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