With the rapid development of microwave photonic technology in recent years, microwave photonic radar can generate and process signals far beyond the relative bandwidth of traditional radar, and can achieve centimeter-level resolution when imaging. However, the echo characteristics of microwave photonic radar are quite different from those of traditional radar, which degrades the performance of traditional imaging algorithms. Therefore, it is crucial to propose an imaging algorithm that is compatible with the characteristics of microwave photonic imaging radars. This paper first summarizes the development of microwave photonic imaging radar. Then analyzes the typical problems in the imaging process of microwave photonic radar, and proposes corresponding solutions to these problems. Finally the processing results of some measured data of microwave photonic imaging radar are shown.
KEYWORDS: Super resolution, Signal to noise ratio, Radar, Radar imaging, Antennas, Reflectors, Convolution, Doppler effect, Data processing, Deconvolution
The iterative shrinkage/thresholding regularization method is an efficient and robust approach to implement super-resolution imaging for forward-looking scanning radar (FLSR). Nevertheless, numerous iterations are normally required to obtain a high super-resolution ratio and thus it can be time consuming. A vector extrapolation accelerated iterative shrinkage/thresholding regularization method is proposed for FLSR. In order to achieve effective acceleration, a predicted point is particularly formulated by linearly extrapolating the historical iterates before conducting the iterative shrinkage/thresholding operation. To reduce the prediction error and stabilize the acceleration process, an adaptively adjusted acceleration step size is derived according to the similarity of the adjacent iterative vectors. Last, a fixed-threshold constraining operator is conducted on the step size to guarantee the convergence of the acceleration method. The results of simulation experiment and real data processing verify the advantage of the proposed method in both the convergence speed and super-resolution performance when compared with the traditional acceleration methods.
For synthetic aperture radar (SAR), ground moving target (GMT) imaging necessitates the compensation of the additional azimuth modulation contributed by the unknown movement of the GMT. That is to say, it is necessary to estimate the Doppler parameters of the GMT without a priori knowledge of the GMT’s motion parameters. This paper presents a Doppler parameter and velocity estimation method to refocus the GMT from its smeared response in SAR image. The main idea of this method is that an azimuth reference function is constructed to do the correlation integral with the azimuth signal of the GMT. And in general, the Doppler parameters of the presumed azimuth reference function are different from those of the GMT’s azimuth signal since the velocity parameters of the GMT are unknown. Therefore, the correlation operation referred to here is actually mismatched, and the processing result of is shifted and defocused. The shifted and defocused result is utilized to get the real Doppler parameters and the velocity parameters of the GMT. One advantage of this method is that it is a nonsearching method. Another advantage is that both the Doppler centroid and the Doppler frequency rate of the GMT can be simultaneously estimated according to the relationships between the Doppler parameters and the smeared response of the GMT. In addition, the velocity of the GMT can also be obtained based on the estimated Doppler parameters. Numerical simulations and experimental data processing verify the validity of the method proposed.
Raw data generation for synthetic aperture radar (SAR) is very powerful for designing systems and testing imaging algorithms. In this paper, a raw data generation method based on Fourier analysis for one-stationary bistatic SAR is presented. In this mode, two-dimensional (2-D) spatial variation is the major problem faced by the fast Fourier transform–based raw data generation. To deal with this problem, a 2-D linearization followed by a 2-D frequency transformation is employed in this method. This frequency transformation can reflect the 2-D spatial variation. Residual phase compensation is also discussed. Numerical simulation verifies the method.
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