Abstract. The inverse synthetic
aperture radar systems are widely applied in many fields, like space and air
control systems, special research laboratories and outdoor measurements
facilities. To provide well-focused inverse synthetic aperture radar images we
need to compensate translational and rotational phase shifts. This effort is also
called range alignment and autofocusing. Autofocusing techniques provide to
compensate nonlinear motion errors of the object. Many autofocus algorithms
have been proposed over the years, ranging from quantitative measurement of
residual errors to qualitative visual comparison. However, different data sets
and motion errors were employed, it is difficult to perform comparative studies
on various algorithms. In this paper a is observed a few of known autofocusing
algorithms based on minimizing image entropy and phase gradient technics
applied to experimental research in inverse synthetic aperture imaging
techniques. Comparison
of autofocusing algorithms was carried out on the mathematical model developed
by the authors. As quality metrics for
assessing algorithms performance
were chosen: the minimum image entropy,
correlation with the standard, computing complexity and work duration. As the result we can recommend these
algorithms to various practical cases in air control, measurement facilities or
laboratories efforts. In conclusion our study is that the algorithms based on minimum
entropy provide the better performance results in most cases.
Key words: inverse synthetic
aperture, radar images, nonparametric autofocusing.
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