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In this paper an application of an autofocusing algorithm, previously developed by the authors for the case of Inverse Synthetic Aperture Radar (ISAR) [i.e. airborne radar targets], is presented here for the case of Synthetic Aperture Radar (SAR) geometry [i.e. ground radar targets]. Here, a new theory and methodology for generating SAR synthetic backscattered data is also developed. This algorithm is named 'CPI-split-algorithm', where CPI stands for 'Coherent Processing Interval'. Moreover, two simulation scenarios are presented for a ship target, which is located on the sea surface. In the first simulation scenario, the ship is considered at first to be stationary, and subsequently an oscillatory movement is induced to its position along the vertical axis, due to sea surface motion. In the second simulation scenario, a partial loss of data is examined, caused by temporary accidental malfunctioning of radar transmitter or receiver, assuming that the target (ship) is stationary. Numerical results presented in this paper show the effectiveness of the proposed autofocusing algorithm for SAR image enhancement.

Autofocus is a technique for improving inverse synthetic aperture radar (ISAR) imaging. In this paper, a novel autofocusing method is developed for high-resolution stepped-frequency ISAR. Non-uniform rotational motion is compensated through the proposed post-processing methodology. In this way, the computational cost of polar reformatting process can be circumvented. The proposed CPI-split autofocusing process results in well-focused ISAR images for high angular acceleration periods. Finally, ISAR image entropy dependencies are thoroughly examined through various simulation results, leading to an acceptable range of entropy values for the autofocusing process. Ill. 7, bibl. 9, tabl. 3 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.110.4.305

Rbsrracr-Time -frequency analysis i s nowadays very rrequently used for the evaluation of non -stationary signals. with applications in areas such as radar target imaging and identification, seismic signal interpretation etc. The corresponding two -dimensional (ZD) time -frequency plots, usually called 'spectrograms', are sometimes very useful, because they provide the time -dependence of the signal spectrum, not available in other traditionat spectrum estimation methods.In this paper we focus on several time -frequency techniques, like the Short -Time Fourier Transform (STFT). Furthermore, the performance of the 3ilinear Time -Frequency Transforms is also carefully e.x.amined.The basic idea of time -frequency analysis is the characterization of the time-varying frequency content of a signal. I n this way, additional signal information can be acquired, and ultimately improved target image resolution can be achieved. Finally, time -frequency transforms allow the use of variable parameters, which chsnge according to the time and frequency, in order to achieve the desired target resolution.In this paper, we develop computer codes for the above methods, and simulated synthetic radar data are used for their implementation.