ISAR imaging method based on matching tracking

Abstract

The invention discloses an ISAR imaging method based on matching tracking, and solves the problem of echo data distance alignment in IASR imaging. The ISAR imaging method comprises the following steps: performing Fourier transform on received echo data in a distance direction; 2, achieving matching tracking on a continuous parameter space by virtue of Chebyshev polynomials construction atoms by using a simulation annealing algorithm, thereby obtaining atoms in optimal matching with echo data; 3, performing motion compensation on the echo data by using the optimal atoms; 4, performing inverse Fourier transform on the echo data in the distance direction, and recovering into a time-domain signal; 5, imaging the echo data by using a self-focusing method. By adopting the ISAR imaging method, a relatively good motion compensation effect can be achieved, and high-resolution ISAR imaging can be achieved.

Description

technical field [0001] The invention belongs to the field of signal processing, in particular to an ISAR imaging method based on matching pursuit. Background technique [0002] Inverse synthetic aperture radar (ISAR) is a two-dimensional high-resolution imaging technology. ISAR is based on the principle of turntable imaging. The key technology to convert moving targets into turntable targets is motion compensation, and the non-cooperative nature of detection targets increases. Difficulty of motion compensation. [0003] At present, a commonly used ISAR imaging technology uses the minimum entropy method to perform envelope alignment on the echo data after the pulse pressure, and then adopts the self-focusing method for imaging. Range alignment is the main part of ISAR imaging motion compensation. The distance alignment method based on minimum entropy is a data-based motion compensation method. It can adaptively complete the distance alignment through iteration and has good r...

AI Technical Summary

Problems solved by technology

However, the distance alignment method based on minimum entropy has three main defects: first, the method of minimum entropy needs to perform pulse compression on the original echo data, and pulse pressure will introduce signal processing loss; second, the signal envelope based on minimum entropy The performance of the alignment method will decrease in the case of low signal-to-clutter ratio, resulting in lower accuracy; third, from the perspective of image entropy, the unaligned few distance units have little impact on image entropy, but have a significant impact on ISAR imaging results , so using entropy to measure the effect of distance alignment will cause a large deviation in ISAR two-dimensional imaging

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