However, this method cannot be applied for the PCA algorithm. (ii) Non-coherent averaging is used in PGA for automatic selection of the window width. For this reason, selecting the strongest target on each range line is not a robust way to estimate phase error. This could be problematic for phase curvature estimation at low signal-to-clutter ratio (SCR). However, there are some problems concerned with the traditional PCA algorithm, which can be mentioned asfollows: (i) the phase curvature in PCA is noisier than the phase gradient of PGA. The PCA is a well-known algorithm with great potential to deal with high-order phase error. In this study, an improved version of PCA is proposed for autofocusing of stripmap SAR mode imagery. proposed PCA to extend PGA for stripmap mode SAR systems. Commonly, phase curvature autofocus (PCA) is applied for stripmap geometry. However, the PGA method cannot be directly applied for image formation of the conventional stripmap mode signal. It uses the redundancy of the phase error across many range cells by averaging them, and it is derived using maximum likelihood (ML) estimation algorithm. PGA is a non-parametric method, which was first proposed in 1989 for spotlight mode SAR image formation. The eigenvector and phase gradient autofocus (PGA) algorithms are among the non-parametric methods that are able to estimate phase errors with higher order. Non-parametric autofocus techniques do not require exact information of the phase errors for estimation. These types of methods are often unable to compensate for high-frequency phase errors because of inaccurate modelling. The mapdrift (MD) and multiple aperture mapdrift are among the best parametric autofocus techniques the performance of these algorithms is only assured when the phase error is properly modelled. Parametric autofocus methods try to estimate the coefficients of an expansion that models the phase error.
In general, autofocus methods can be divided into two categories: parametric and non-parametric. Autofocus can be described as the process of automatic estimation and compensation of residual phase error in SAR images. Īfter navigation-based MC, autofocus methods are usually applied to enhance image focus by compensating residual phase error. removal of partial phase error and the residual range cell migration. However, low-rate motion measurements systems provide only coarse MC, e.g. This would surpass the accuracy of navigation systems. In order to achieve highly precise MC, these measurements have to be executed from pulse to pulse at sub-wavelength scale. To avoid this, an inertial navigation unit (INU) and a global positioning system (GPS) are usually used to provide real-time data for motion compensation (MC) in SAR systems. SAR image formation of data with uncompensated phase errors causes a severe loss of geometry accuracy and degrades image quality.
Main reasons for these phase variations consist of oscillator and other subsystem phase instabilities, uncompensated sensor motion, and atmospheric propagation. Real data experiments demonstrate the success of the proposed autofocus method, which is applied to the stretched-based pulsed mode SAR data set in the absence of highly accurate inertial navigation units.Ĭompensation for adverse variations in the synthetic aperture radar (SAR) phase history is a major challenge in SAR signal processing development. In this study, the modification of traditional PCA algorithm has been performed in different steps including the following: improving range-compressed data, prominent points extraction, adaptive windowing, weighted maximum likelihood for phase error estimation, improving phase error result, range shift compensation, and determining the condition to end the iterations. The main problems concerned with the traditional PCA algorithm are related to selecting candidates in the image for phase error estimation, windowing, estimation procedure, and range shift due to the phase error. PCA method was proposed to extend the phase gradient autofocus method for SAR systems in stripmap mode. IET Generation, Transmission & Distributionīased on the theory of phase curvature autofocus (PCA) on stripmap synthetic aperture radar (SAR), an improved algorithm for increasing the accuracy of phase error compensation is presented in this study.IET Electrical Systems in Transportation.IET Cyber-Physical Systems: Theory & Applications.IET Collaborative Intelligent Manufacturing.CAAI Transactions on Intelligence Technology.